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OMOTOSHO, LAWRENCE, IBRAHIM OGUNDOYIN, OLAJIDE ADEBAYO, and JOSHUA OYENIYI. "AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK." Journal of Engineering Studies and Research 27, no. 2 (October 10, 2021): 73–81. http://dx.doi.org/10.29081/jesr.v27i2.276.
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Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.
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Ramana N., Venkata, S. Anu H. Nair, and K. P. Sanal Kumar. "Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (December 9, 2022): 99–107. http://dx.doi.org/10.17762/ijritcc.v10i1s.5798.
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Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models.
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Haider, Syed Aqeel, Yawar Rehman, and S. M. Usman Ali. "Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics." Electronics 9, no. 11 (November 14, 2020): 1916. http://dx.doi.org/10.3390/electronics9111916.
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In the proposed study, we examined a multimodal biometric system having the utmost capability against spoof attacks. An enhanced anti-spoof capability is successfully demonstrated by choosing hand-related intrinsic modalities. In the proposed system, pulse response, hand geometry, and finger–vein biometrics are the three modalities of focus. The three modalities are combined using a fuzzy rule-based system that provides an accuracy of 92% on near-infrared (NIR) images. Besides that, we propose a new NIR hand images dataset containing a total of 111,000 images. In this research, hand geometry is treated as an intrinsic biometric modality by employing near-infrared imaging for human hands to locate the interphalangeal joints of human fingers. The L2 norm is calculated using the centroid of four pixel clusters obtained from the finger joint locations. This method produced an accuracy of 86% on the new NIR image dataset. We also propose finger–vein biometric identification using convolutional neural networks (CNNs). The CNN provided 90% accuracy on the new NIR image dataset. Moreover, we propose a robust system known as the pulse response biometric against spoof attacks involving fake or artificial human hands. The pulse response system identifies a live human body by applying a specific frequency pulse on the human hand. About 99% of the frequency response samples obtained from the human and non-human subjects were correctly classified by the pulse response biometric. Finally, we propose to combine all three modalities using the fuzzy inference system on the confidence score level, yielding 92% accuracy on the new near-infrared hand images dataset.
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Oh, Junhyoung, Ukjin Lee, and Kyungho Lee. "Usability Evaluation Model for Biometric System considering Privacy Concern Based on MCDM Model." Security and Communication Networks 2019 (March 27, 2019): 1–14. http://dx.doi.org/10.1155/2019/8715264.
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Biometric devices play an integral role in consumer’s daily life, providing a seamless environment. However, it is essential to measure the usability of biometrics, owing to the elements of biometrics satisfying both usability and security. This study redefines the elements of biometrics pertaining to usability determined in previous studies and adds elements of psychological relevance, such as privacy concerns. To organize the interrelated usability structure systemically, this paper applies the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) to derive the usability structure. Thereupon, the established structure is applied in the clustered weighted Analytical Network Processes (ANP) to generate the proposed usability evaluation model. By these methods, the pertinent relationships between the factors are clarified and the weight of each element is determined. In the empirical study, 106 students measured usability of the fingerprint recognition system, iris recognition system, and facial recognition system employing our usability evaluation model. The results of this model generate the quantitative score of usability for biometric systems and suggest strategies to increase the score. The proposed usability evaluation model can comprehensively assist usability practitioners to evaluate biometric systems.
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Drosou, A., D. Ioannidis, K. Moustakas, and D. Tzovaras. "Unobtrusive Behavioral and Activity-Related Multimodal Biometrics: The ACTIBIO Authentication Concept." Scientific World JOURNAL 11 (2011): 503–19. http://dx.doi.org/10.1100/tsw.2011.51.
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Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.
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Pontoh, Fransisca J., Jayanti Yusmah Sari, Amil A. Ilham, and Ingrid Nurtanio. "MULTISPECTRAL DORSAL HAND VEIN RECOGNITION BASED ON LOCAL LINE BINARY PATTERN." Jurnal Ilmu Komputer dan Informasi 11, no. 2 (June 29, 2018): 95. http://dx.doi.org/10.21609/jiki.v11i2.576.
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Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%.
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Karmakar, Dhiman, Madhura Datta, and C. A. Murthy. "Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique." International Journal of Software Science and Computational Intelligence 5, no. 3 (July 2013): 22–32. http://dx.doi.org/10.4018/ijssci.2013070102.
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Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique.
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D S, Dr Dinesh Kumar. "Human Authentication using Face, Voice and Fingerprint Biometrics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 853–62. http://dx.doi.org/10.22214/ijraset.2021.36381.
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Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this project, we present a multimodal biometric system that is based on the fusion of face, voice and fingerprint biometrics. For face recognition, we employ Haar Cascade Algorithm, while minutiae extraction is used for fingerprint recognition and we will be having a stored code word for the voice authentication, if any of these two authentication becomes true, the system consider the person as authorized person. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.
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Jain, Anil K., and Arun Ross. "Bridging the gap: from biometrics to forensics." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1674 (August 5, 2015): 20140254. http://dx.doi.org/10.1098/rstb.2014.0254.
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Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large.
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Arunachalamand, MuthuKumar, and Kavipriya Amuthan. "Finger Knuckle Print Recognition using MMDA with Fuzzy Vault." International Arab Journal of Information Technology 17, no. 4 (July 1, 2020): 554–61. http://dx.doi.org/10.34028/iajit/17/4/14.
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Currently frequent biometric scientific research such as with biometric applications like face, iris, voice, hand-based biometrics traits like palm print and fingerprint technique are utilized for spotting out the persons. These specific biometrics habits have their own improvement and weakness so that no particular biometrics can adequately opt for all terms like the accuracy and cost of all applications. In recent times, in addition, to distinct with the hand-based biometrics technique, Finger Knuckle Print (FKP) has been appealed to boom the attention among biometric researchers. The image template pattern formation of FKP embraces the report that is suitable for spotting the uniqueness of individuality. This FKP trait observes a person based on the knuckle print and the framework in the outer finger surface. This FKP feature determines the line anatomy and finger structures which are well established and persistent throughout the life of an individual. In this paper, a novel method for personal identification will be introduced, along with that data to be stored in a secure way has also been proposed. The authentication process includes the transformation of features using 2D Log Gabor filter and Eigen value representation of Multi-Manifold Discriminant Analysis (MMDA) of FKP. Finally, these features are grouped using k-means clustering for both identification and verification process. This proposed system is initialized based on the FKP framework without a template based on the fuzzy vault. The key idea of fuzzy vault storing is utilized to safeguard the secret key in the existence of random numbers as chaff pints
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Ayanaba, Rasheed Abubakar. "Image-assisted Biometric Identification." Advances in Multidisciplinary and scientific Research Journal Publication 1, no. 1 (July 24, 2022): 131–38. http://dx.doi.org/10.22624/aims/crp-bk3-p22.
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Biometrics is a rapidly developing technology that has seen widespread use in forensics applications such as criminal identification, secure access, and prison security. A biometric system is a pattern recognition system that recognizes a person by determining the authenticity of a physiological and/or behavioural feature that that person possesses. One of the most widely accepted biometrics utilized by humans in their visual interactions is image- assisted based (facial) biometric. Image-assisted biometric identification is the use of face recognition technology in capturing image of a unique feature of an individual such as an eye or face, and comparing it with a template captured earlier and stored a database. Face recognition is one of the more recent biometrics technologies. The system examines face features and tries to match them to a database of digitized images. This technology is quite new, having only been available commercially since the 1990s. Face recognition has gotten a lot of press after the 9/11 attacks because of its capacity to identify known terrorists and criminals. [1]. Although the technology is mostly utilized for security and law enforcement, there is growing interest in other applications. Keyword: Image-assisted based biometric identification, Face recognition technology, Image
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Shah, Shriyasti, Keshav Sharma, Roopam Gupta, and Manjot Kaur Bhatia. "Face Recognition based Attendance Management System." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 437–39. http://dx.doi.org/10.22214/ijraset.2022.47913.
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Abstract: Biometrics are unique, measurable characteristics of an individual that can be used to automatically recognize an individual or to verify an individual's identity. Biometrics can measure both physiological and behavioural characteristics. Physiological biometrics (based on measurements of parts of the human body and data obtained from direct measurements) include finger scans, face recognition, iris scans, retina scans, and hand scans. Everything happens online as technology advances around the world. Facial recognition systems are used to identify people in photos, videos, in real time, etc. It is a category of biometric security. It provides an environment with privacy and authentication that helps organizations keep their data safe. It can be used for security, authentication, identification, accuracy and many other benefits. It can also be used because it is a non-contact, non-invasive procedure. In addition, facial recognition systems can also be used for attendance assessment in schools, colleges, offices, etc. Since the conventional manual attendance system takes time and effort to maintain, it can be used as a class attendance system that uses the concept of face recognition or for attendance scoring within the company, minimizing the burden on administrators. I can. Attendance is on your own and no substitutes are allowed. Therefore, the need for this system is increasing. The system consists of four phases: recording, training, facial recognition, and attendance update. A record is created when a new user uses the software to enroll their face and is assigned an ID. During training, the dataset created is used as input to a training model that uses a k-NN classifier to classify the images present in the datase
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Reddy, M. V. Bramhananda, and V. Goutham. "IRIS TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC SYSTEMS FOR UNIQUE HUMAN IDENTIFICATION." International Journal of Research -GRANTHAALAYAH 6, no. 1 (January 31, 2018): 80–90. http://dx.doi.org/10.29121/granthaalayah.v6.i1.2018.1596.
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Biometric features are widely used in real time applications for unique human identification. Iris is one of the physiological biometric features which are regarded as highly reliable in biometric identification systems. Often iris is combined with other biometric features for robust biometric systems. It is also observed that biometrics is combined with cryptography for stronger security mechanisms. Since iris is unique for all individuals across the globe, many researchers focused on using iris or along with other biometrics for security with great precision. Multimodal biometric systems came into existence for better accuracy in human authentication. However, iris is considered to be most discriminatory of facial biometrics. Study of iris based human identification in ideal and non-cooperative environments can provide great insights which can help researchers and organizations that depend on iris-based biometric systems. The technical knowhow of iris strengths and weaknesses can be great advantage. This is more important in the wake of widespread use of smart devices which are vulnerable to attacks. This paper throws light into various iris-based biometric systems, issues with iris in the context of texture comparison, cancellable biometrics, iris in multi-model biometric systems, iris localization issues, challenging scenarios pertaining to accurate iris recognition and so on.
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Alam, Varisha. "Ordering of Huge Biometric Information in Database System." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 2 (June 6, 2021): 1–19. http://dx.doi.org/10.54060/jieee/002.02.011.
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The word biometrics is derived from the Greek words 'bios' and 'metric' which means living and calculation appropriately. Biometrics is the electronic identification of individuals based on their physiological and biological features. Biometric attributes are data take out from biometric test which can be used for contrast with a biometric testimonial. Biometrics composed methods for incomparable concede humans based upon one or more inherent material or behavioral characteristics. In Computer Science, bio-metrics is employed as a kind of recognition access management and access command. Biometrics has quickly seemed like an auspicious technology for attestation and has already found a place in the most sophisticated security areas. A systematic clustering technique has been there for partitioning huge biometric databases throughout recognition. As we tend to are still obtaining the higher bin-miss rate, so this work is predicated on conceiving an ordering strategy for recognition of huge biometric database and with larger precision. This technique is based on the modified B+ tree that decreases the disk accesses. It reduced the information retrieval time and feasible error rates. The ordering technique is employed to proclaims a person’s identity with a reduced rate of differentiation instead of searching the whole database. The response time degenerates, further-more because the accuracy of the system deteriorates as the size of the database increases. Hence, for vast applications, the requirement to reduce the database to a little fragment seems to attain higher speeds and improved accuracy.
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Yang, Wencheng, Song Wang, Jiankun Hu, Guanglou Zheng, and Craig Valli. "Security and Accuracy of Fingerprint-Based Biometrics: A Review." Symmetry 11, no. 2 (January 28, 2019): 141. http://dx.doi.org/10.3390/sym11020141.
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Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper.
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Joseph, Annie Anak, Alex Ng Ho Lian, Kuryati Kipli, Kho Lee Chin, Dayang Azra Awang Mat, Charlie Sia Chin Voon, David Chua Sing Ngie, and Ngu Sze Song. "Person Verification Based on Multimodal Biometric Recognition." Pertanika Journal of Science and Technology 30, no. 1 (November 24, 2021): 161–83. http://dx.doi.org/10.47836/pjst.30.1.09.
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Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system.
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Salama, Gerges M., Safaa El-Gazar, Basma Omar, Rana M. Nassar, Ashraf A. M. Khalaf, Ghada M. El-banby, Hesham F. A. Hamed, Walid El-shafai, and Fathi E. Abd el-samie. "Cancelable biometric system for IoT applications based on optical double random phase encoding." Optics Express 30, no. 21 (September 28, 2022): 37816. http://dx.doi.org/10.1364/oe.466101.
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The security issue is essential in the Internet-of-Things (IoT) environment. Biometrics play an important role in securing the emerging IoT devices, especially IoT robots. Biometric identification is an interesting candidate to improve IoT usability and security. To access and control sensitive environments like IoT, passwords are not recommended for high security levels. Biometrics can be used instead, but more protection is needed to store original biometrics away from invaders. This paper presents a cancelable multimodal biometric recognition system based on encryption algorithms and watermarking. Both voice-print and facial images are used as individual biometrics. Double Random Phase Encoding (DRPE) and chaotic Baker map are utilized as encryption algorithms. Verification is performed by estimating the correlation between registered and tested models in their cancelable format. Simulation results give Equal Error Rate (EER) values close to zero and Area under the Receiver Operator Characteristic Curve (AROC) equal to one, which indicates the high performance of the proposed system in addition to the difficulty to invert cancelable templates. Moreover, reusability and diversity of biometric templates is guaranteed.
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Yaacob, Mohd Noorulfakhri, Syed Zulkarnain Syed Idrus, Wan Azani Wan Mustafa, Mohd Aminudin Jamlos, and Mohd Helmy Abd Wahab. "Identification of the Exclusivity of Individual’s Typing Style Using Soft Biometric Elements." Annals of Emerging Technologies in Computing 5, no. 5 (March 20, 2021): 10–26. http://dx.doi.org/10.33166/aetic.2021.05.002.
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Biometric is used as a main security fence in a computer system. The unique characteristics of a person can be distinguished from each other. Human’s biometrics can be categorized into three types: morphological, biological and behavioural. Morphological biometrics uses physical features for recognition. Biological biometrics used to identify user based on biological features. Behavioural biometrics such as gender, culture, height and weight can be used as an additional security measure within a system. These biometric behavioural features are also known as soft biometric. This study uses soft biometric elements (gender, culture, region of birth and educational level) in the keystroke dynamic study to distinguish typing patterns in each of these categories. The Support Vector Machine (SVM) classification method is used to perform this classification for soft biometric identification. The results of this study have shown that soft biometrics in keystroke dynamic can be used to distinguish group of individuals typing.
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Zhang, Yunxia, Xin Li, Changming Zhao, Wenyin Zheng, Manqing Wang, Yongqing Zhang, Hongjiang Ma, and Dongrui Gao. "Affective EEG-Based Person Identification Using Channel Attention Convolutional Neural Dense Connection Network." Security and Communication Networks 2021 (November 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/7568460.
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In the biometric recognition mode, the use of electroencephalogram (EEG) for biometric recognition has many advantages such as anticounterfeiting and nonsteal ability. Compared with traditional biometrics, EEG biometric recognition is safer and more concealed. Generally, EEG-based biometric recognition is to perform person identification (PI) through EEG signals collected by performing motor imagination and visual evoked tasks. The aim of this paper is to improve the performance of different affective EEG-based PI using a channel attention mechanism of convolutional neural dense connection network (CADCNN net) approach. Channel attention mechanism (CA) is used to handle the channel information from the EEG, while convolutional neural dense connection network (DCNN net) extracts the unique biological characteristics information for PI. The proposed method is evaluated on the state-of-the-art affective data set HEADIT. The results indicate that CADCNN net can perform PI from different affective states and reach up to 95%-96% mean correct recognition rate. This significantly outperformed a random forest (RF) and multilayer perceptron (MLP). We compared our method with the state-of-the-art EEG classifiers and models of EEG biometrics. The results show that the further extraction of the feature matrix is more robust than the direct use of the feature matrix. Moreover, the CADCNN net can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.
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Zhang, Yanqiang, Dongmei Sun, and Zhengding Qiu. "Hand-based single sample biometrics recognition." Neural Computing and Applications 21, no. 8 (January 19, 2011): 1835–44. http://dx.doi.org/10.1007/s00521-011-0521-x.
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Rukhiran, Meennapa, Sorapak Pukdesree, and Paniti Netinant. "Biometric Cloud Services for Web-Based Examinations." International Journal of Information Technology and Web Engineering 17, no. 1 (January 2022): 1–25. http://dx.doi.org/10.4018/ijitwe.299022.
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Biometric recognition may be used in conjunction with human authentication on a smartphone to improve accuracy, reliability, and simplicity, and to aid in fraud prevention and user authentication. While single biometric authentication addresses environmental degradation and sensor noise limitations, and the single point of failure scenario in biometric systems can result in more robust biometric systems, multimodal biometric authentication can improve the accuracy of identification and recognition. The purpose of this research is to propose a facial and speech authentication system that is cloud-based and supports a web-based examination approach. The system enables students' biometrics to be registered, students to be recognized, and student recognition results to be reported. The confusion matrix is used to compare the results of positive and negative detection in various ways, including accuracy score, precision value, and recall value. Adaptive multimodal biometric authentication should be designed and evaluated for further research using the optimal weights for each biometric.
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Danish, Aafreen, Khushali Hedau, Diksha Ukey, Anisha Walde, Uzma Sohail Sheikh, and Prof Akbar Nagani. "Fingerprint,Face and Voice Recognition Based Attendance Monitoring System." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 749–52. http://dx.doi.org/10.22214/ijraset.2022.41346.
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Abstract: In This project aims to record the attendance without manual intervention. In the area of technology that changes and modifies daily, use biometrics is the most popular and trending technology. Taking attendance manually for a class of almost 60-80 students can be a time-consuming task if thought of it in a long run Each person has a unique biometric feature such as fingerprint, face structure, voice detection etc. Keywords— Boimetrics, Fringerprint, Face Structure,Voice Detection.
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Shende, Priti, and Yogesh Dandawate. "Multimodal biometric identification system with deep learning based feature level fusion using maximum orthogonal method." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 4 (February 18, 2022): 429–37. http://dx.doi.org/10.3233/kes-210086.
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Multimodal Biometrics are used to developed the robust system for Identification. Biometric such as face, fingerprint and palm vein are used for security purposes. In this Proposed System, Convolutional neural network is used for recognizing the image features. Convolutional neural networks are complex feed forward neural networks used for image classification and recognition due to its high accuracy rate. Convolutional neural network extracts the features of face, fingerprint and palm vein. Feature level fusion is done at Rectified linear unit layer. Maximum orthogonal component method is used for Fusion. In Maximum orthogonal component method, prominent features of biometrics are considered and fused together. This method helps to improve the recognition rates. Database are self-generated using these biometrics. Training and Testing is done using 4500 images of face, fingerprint and palm vein. Performance parameters are improved by this technique. The experimental results are better than conventional methods.
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Buciu, Ioan, and Alexandru Gacsadi. "Biometrics Systems and Technologies: A survey." International Journal of Computers Communications & Control 11, no. 3 (March 24, 2016): 315. http://dx.doi.org/10.15837/ijccc.2016.3.2556.
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In a nutshell, a biometric security system requires a user to provide some biometric features which are then verified against some stored biometric templates. Nowadays, the traditional password based authentication method tends to be replaced by advanced biometrics technologies. Biometric based authentication is becoming increasingly appealing and common for most of the human-computer interaction devices. To give only one recent example, Microsoft augmented its brand new Windows 10 OS version with the capability of supporting face recognition when the user login in. This chapter does not intend to cover a comprehensive and detailed list of biometric techniques. The chapter rather aims at briefly discussing biometric related items, including principles, definitions, biometric modalities and technologies along with their advantages, disadvantages or limitations, and biometric standards, targeting unfamiliar readers. It also mentions the attributes of a biometric system as well as attacks on biometrics. Important reference sources are pointed out so that the interested reader may gain deeper in-depth knowledge by consulting them.
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Bacchini, Fabio, and Ludovica Lorusso. "A tattoo is not a face. Ethical aspects of tattoo-based biometrics." Journal of Information, Communication and Ethics in Society 16, no. 2 (May 14, 2018): 110–22. http://dx.doi.org/10.1108/jices-05-2017-0029.
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Purpose This study aims to explore the ethical and social issues of tattoo recognition technology (TRT) and tattoo similarity detection technology (TSDT), which are expected to be increasingly used by state and local police departments and law enforcement agencies. Design/methodology/approach The paper investigates the new ethical concerns raised by tattoo-based biometrics on a comparative basis with face-recognition biometrics. Findings TRT raises much more ethically sensitive issues than face recognition, because tattoos are meaningful biometric traits, and tattoo identification is tantamount to the identification of many more personal features that normally would have remained invisible. TSDT’s assumption that classifying people in virtue of their visible features is useful to foretell their attitudes and behaviours is dangerously similar to racist thought. Practical implications The findings hope to promote an active debate on the ethical and social aspects of tattoo-based biometrics before it is intensely implemented by law enforcement agencies. Social implications Tattooed individuals – inasmuch as they are more controlled and monitored – are negatively discriminated in comparison to un-tattooed individuals. As tattooing is not uniformly distributed among population, many demographic groups like African–Americans will be overrepresented in tattoos databases used by TRT and TSDT, thus being affected by disproportionately higher risk to be found as a match for a given suspect. Originality/value TRT and TSDT represent one of the new frontiers of biometrics. The ethical and social issues raised by TRT and TSDT are currently unexplored.
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Qin, Huafeng, and Peng Wang. "A Template Generation and Improvement Approach for Finger-Vein Recognition." Information 10, no. 4 (April 18, 2019): 145. http://dx.doi.org/10.3390/info10040145.
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Finger-vein biometrics have been extensively investigated for person verification. One of the open issues in finger-vein verification is the lack of robustness against variations of vein patterns due to the changes in physiological and imaging conditions during the acquisition process, which results in large intra-class variations among the finger-vein images captured from the same finger and may degrade the system performance. Despite recent advances in biometric template generation and improvement, current solutions mainly focus on the extrinsic biometrics (e.g., fingerprints, face, signature) instead of intrinsic biometrics (e.g., vein). This paper proposes a weighted least square regression based model to generate and improve enrollment template for finger-vein verification. Driven by the primary target of biometric template generation and improvement, i.e., verification error minimization, we assume that a good template has the smallest intra-class distance with respect to the images from the same class in a verification system. Based on this assumption, the finger-vein template generation is converted into an optimization problem. To improve the performance, the weights associated with similarity are computed for template generation. Then, the enrollment template is generated by solving the optimization problem. Subsequently, a template improvement model is proposed to gradually update vein features in the template. To the best of our knowledge, this is the first proposed work of template generation and improvement for finger-vein biometrics. The experimental results on two public finger-vein databases show that the proposed schemes minimize the intra-class variations among samples and significantly improve finger-vein recognition accuracy.
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Maiorana, Emanuele, Patrizio Campisi, and Alessandro Neri. "Template Protection and Renewability for Dynamic Time Warping Based Biometric Signature Verification." International Journal of Digital Crime and Forensics 1, no. 4 (October 2009): 40–57. http://dx.doi.org/10.4018/jdcf.2009062404.
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In this article, the authors propose a protected on-line signature based biometric authentication system, where the original signature templates are protected by transforming them in a non-invertible way. Recovering the original biometrics from the stored data is thus computationally as hard as random guessing them. The transformed templates are compared employing a Dynamic Time Warping (DTW) matching strategy. The reported experimental results, evaluated on the public MCYT signature database, show that the achievable recognition rates are only slightly affected by the proposed protection scheme, which is able to guarantee the desired security and renewability for the considered biometrics.
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Jayaram, M. A., G. K. Prashanth, and Sachin C. Patil. "Inertia-Based Ear Biometrics: A Novel Approach." Journal of Intelligent Systems 25, no. 3 (July 1, 2016): 401–16. http://dx.doi.org/10.1515/jisys-2015-0047.
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AbstractThe human ear has been deemed to be a source of data for person identification in recent years. Ear biometrics has distinct advantages, such as visibility from a distance and ease with which images could be captured. This paper elaborates on a novel approach to ear biometrics. We propose moment of inertia-based biometric for the ears in any random orientation. The features concerned are the moment of inertia about the major and minor axes, corresponding radii of gyration, and the planar surface area of the ear. The databases of the said features were collected through ear images of 600 subjects. Principal component analysis of the features demonstrated that the radius of gyration with respect to the major axis, moment of inertia about the minor axis, and radius of gyration about the minor axis are significant attributes contributing to major variability. The person identification system developed showed recognition rates of 99% with just three attributes, when compared with the 96% recognition rate when all five attributes were considered. The evaluation of the system was done on several metrics. All metrics were found to be insignificant in their magnitude, which is suggestive of robustness and excellent authentication performance.
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Jain, Rubal, and Chander Kant. "Attacks on Biometric Systems: An Overview." International Journal of Advances in Scientific Research 1, no. 7 (September 3, 2015): 283. http://dx.doi.org/10.7439/ijasr.v1i7.1975.
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Biometrics is a pattern recognition system that refers to the use of different physiological (face, fingerprints, etc.) and behavioral (voice, gait etc.) traits for identification and verification purposes. A biometrics-based personal authentication system has numerous advantages over traditional systems such as token-based (e.g., ID cards) or knowledge-based (e.g., password) but they are at the risk of attacks. This paper presents a literature review of attack system architecture and makes progress towards various attack points in biometric system. These attacks may compromise the template resulting in reducing the security of the system and motivates to study existing biometric template protection techniques to resist these attacks.
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Mudhafer Taher Al Mossawy, Mais, and Loay E. George. "A digital signature system based on hand geometry - Survey." Wasit Journal of Computer and Mathematics Science 1, no. 1 (April 1, 2022): 1–14. http://dx.doi.org/10.31185/wjcm.vol1.iss1.18.
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In recent years large number of emerging automated applications faces the need to have recognition abilities of persons using their own self biometrics, before they can access the applications services. Nowadays, Biometric recognition is used, it can be used as automatic identification or automatic verification of persons based on their physiological or behavioral characteristics. There are no perfect biometric measurements;each biometry has its advantages and limitations. Each biometry requires specific vital identity to answer the identification or verification question. The suitability of a particular biometry for a particular application depends on many factors. Hand geometry/shape is a very simple biometric technology that uses the measurements of human hand to verify the identity of the individuals. The measurements include the distance between certain mark points, shape and width of fingers and size of palm. The biometric systems that employing hand geometry become widely used since they have high public acceptance. This article aims to survey several articles found in literature about hand based biometric system, and to compare different methods of biometric recognition that based on hand geometry.
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Ohnuma, Kazuhiko. "Novel biometrics based on nose pore recognition." Optical Engineering 48, no. 5 (May 1, 2009): 057204. http://dx.doi.org/10.1117/1.3130242.
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Wang, Feng. "Fusion Fingerprint and Face Multi-Biometrics Recognition Based on D-S Evidence Theory." Advanced Materials Research 459 (January 2012): 644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.459.644.
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Single biometric feature has not meet people's needs. After analyzing fingerprints and face recognition technology, a fused theory is proposed which comprise fingerprints and face multi-biometrics features recognition based on D-S evidence theory. This article first analysis main part of the Face and Fingerprint Identification System, then gives a decision-making on integration in the face and fingerprint recognition method. In this paper we divide minutia features of fingerprint into certain and uncertain region which could make the performance of verification in certain region better than the original performance. By this fusion strategy the whole performance is improved.
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Chiou, Shin-Yan. "Secure Method for Biometric-Based Recognition with Integrated Cryptographic Functions." BioMed Research International 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/623815.
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Biometric systems refer to biometric technologies which can be used to achieve authentication. Unlike cryptography-based technologies, the ratio for certification in biometric systems needs not to achieve 100% accuracy. However, biometric data can only be directly compared through proximal access to the scanning device and cannot be combined with cryptographic techniques. Moreover, repeated use, improper storage, or transmission leaks may compromise security. Prior studies have attempted to combine cryptography and biometrics, but these methods require the synchronization of internal systems and are vulnerable to power analysis attacks, fault-based cryptanalysis, and replay attacks. This paper presents a new secure cryptographic authentication method using biometric features. The proposed system combines the advantages of biometric identification and cryptographic techniques. By adding a subsystem to existing biometric recognition systems, we can simultaneously achieve the security of cryptographic technology and the error tolerance of biometric recognition. This method can be used for biometric data encryption, signatures, and other types of cryptographic computation. The method offers a high degree of security with protection against power analysis attacks, fault-based cryptanalysis, and replay attacks. Moreover, it can be used to improve the confidentiality of biological data storage and biodata identification processes. Remote biometric authentication can also be safely applied.
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Arunarani, S., and R. Gobinath. "Literature review on multimodal biometrics." International Journal of Engineering & Technology 7, no. 2.26 (May 7, 2018): 31. http://dx.doi.org/10.14419/ijet.v7i2.26.12529.
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As technological reformation is widen, biometric systems substitute knowledge based and token based recognition systems. Confidential data are accessed by the user after the user is recognized by biometric systems. Efforts have been made to acquire more suitable prototype for recognizing human as multimodal biometrics has more severe concern because of noise in the sample and malfunctioning sensing devices. This paper gives a dual study related to multimodal biometrics, including a literature review of the prior work in authentication and the proposed evaluation approaches. First, we classify few epitome studies considered in last decades to show how this problem has been solved until now. Second, the paper gives a introduction about basic principles of the associated evaluation approaches, and then provide an extended evaluation framework based on the enrollment selection and also statistically convincing measures for evaluating quality metrics.
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Sheth, Abhishek Arun, Meghana Sharath, Avula Sai Charan Reddy, and Sindhu K. "Gait Recognition Using Convolutional Neural Network." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 01 (January 17, 2023): 107–18. http://dx.doi.org/10.3991/ijoe.v19i01.33823.
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Biometrics are the body measurements and calculations related to individuals. Biometrics validation is used as a form of identification of individual. Gait recognition system is one of the most advanced technology that people have been working on for a while now that takes center stage in the field of biometrics. Compared to the other types of existing systems of biometric recognition such as fingerprint detection, iris-scanning systems etc., Gait Recognition system ensures no human intervention. This paper focuses on recognition based on a person’s gait. Every person has a distinct gait pattern that is unique to every other person. To train the model CASIA-B dataset has been used. The dataset includes 124 subjects where each sample has undergone Gait Energy Image extraction. Samples with clothing and baggage have been included which changes the silhouette of the person. Therefore, the model has been trained for a wider application where people wear different type of clothing and carry-ons. A Convolutional Neural Network consisting of 8 layers has been trained which performs well on both samples of dataset and an accuracy of 95.45% was obtained on dataset not involving layers of clothing and accuracy of 91.80% was obtained for the sample with clothing and baggage.
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Singh, Law Kumar, Munish Khanna, and Hitendra Garg. "Multimodal Biometric Based on Fusion of Ridge Features with Minutiae Features and Face Features." International Journal of Information System Modeling and Design 11, no. 1 (January 2020): 37–57. http://dx.doi.org/10.4018/ijismd.2020010103.
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Multimodal biometrics refers to the exploiting combination of two or more biometric modalities in an identification of a system. Fingerprint, face, retina, iris, hand geometry, DNA, and palm print are physiological traits while voice, signature, keystrokes, gait are behavioural traits used for identification by a system. Single biometric features like faces, fingerprints, irises, retinas, etc., deteriorate or change with time, environment, user mode, physiological defects, and circumstance therefore integrating multi features of biometric traits increase robustness of the system. The proposed multimodal biometrics system presents recognition based on face detection and fingerprint physiological traits. This proposed system increases the efficiency, accuracy and decreases execution time of the system as compared to the existing systems. The performance of proposed method is reported in terms of parameters such as False Rejection Rate (FRR), False Acceptance Rate (FAR) and Equal Error Rate (EER) and accuracy is reported at 95.389%.
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Елена Юрьевна, Фролова, and Кошлыкова Юлия Александровна. "HUMAN IDENTIFICATION BASED ON BIOMETRIC DATA: A REVIEW OF MODERN TECHNOLOGIES." NORTH CAUCASUS LEGAL VESTNIK 1, no. 3 (September 2022): 167–74. http://dx.doi.org/10.22394/2074-7306-2022-1-3-167-174.
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Biometric identification systems have now become quite widespread, since they use not specialized physical media for recognition, but signs or features of the person himself, which makes it possible to reliably identify a person for a given purpose. Biometric identification is the process of comparing and determining the similarity between a person's data and his biometric "template". Biometrics allows you to identify and verify a person based on a set of specific and unique traits inherent in him from birth. This article discusses certain types of biometric data to be identified, problems and prospects of their application.
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Mashagba, Eman Fares Al. "Human Identification Based on Geometric Feature Extraction Using a Number of Biometric Systems Available: Review." Computer and Information Science 9, no. 2 (May 2, 2016): 140. http://dx.doi.org/10.5539/cis.v9n2p140.
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<span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-ansi-language: EN-US; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Biometric technology has attracted much attention in biometric recognition. Significant online and offline applications satisfy security and human identification based on this technology. Biometric technology identifies a human based on unique features possessed by a person. Biometric features may be physiological or behavioral. A physiological feature is based on the direct measurement of a part of the human body such as a fingerprint, face, iris, blood vessel pattern at the back of the eye, vascular patterns, DNA, and hand or palm scan recognition. A behavioral feature is based on data derived from an action performed by the user. Thus, this feature measures the characteristics of the human body such as signature/handwriting, gait, voice, gesture, and keystroke dynamics. A biometric system is performed as follows: acquisition, comparison, feature extraction, and matching. The most important step is feature extraction, which determines the performance of human identification. Different methods are used for extraction, namely, appearance- and geometry-based methods. This paper reports on a review of human identification based on geometric feature extraction using several biometric systems available. We compared the different biometrics in biometric technology based on the geometric features extracted in different studies. Several biometric approaches have more geometric features, such as hand, gait, face, fingerprint, and signature features, compared with other biometric technology. Thus, geometry-based method with different biometrics can be applied simply and efficiently. The eye region extracted from the face is mainly used in face recognition. In addition, the extracted eye region has more details as the iris features.</span>
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Mota, Mariana R. F., Pedro H. L. Silva, Eduardo J. S. Luz, Gladston J. P. Moreira, Thiago Schons, Lauro A. G. Moraes, and David Menotti. "A deep descriptor for cross-tasking EEG-based recognition." PeerJ Computer Science 7 (May 19, 2021): e549. http://dx.doi.org/10.7717/peerj-cs.549.
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Due to the application of vital signs in expert systems, new approaches have emerged, and vital signals have been gaining space in biometrics. One of these signals is the electroencephalogram (EEG). The motor task in which a subject is doing, or even thinking, influences the pattern of brain waves and disturb the signal acquired. In this work, biometrics with the EEG signal from a cross-task perspective are explored. Based on deep convolutional networks (CNN) and Squeeze-and-Excitation Blocks, a novel method is developed to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification. The Physionet EEG Motor Movement/Imagery Dataset is used here for method evaluation, which has 64 EEG channels from 109 subjects performing different tasks. Since the volume of data provided by the dataset is not large enough to effectively train a Deep CNN model, it is also proposed a data augmentation technique to achieve better performance. An evaluation protocol is proposed to assess the robustness regarding the number of EEG channels and also to enforce train and test sets without individual overlapping. A new state-of-the-art result is achieved for the cross-task scenario (EER of 0.1%) and the Squeeze-and-Excitation based networks overcome the simple CNN architecture in three out of four cross-individual scenarios.
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Rajasingh, J. Paul, and D. Sai Yaswanth. "Fingerprint Authentication." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 87–89. http://dx.doi.org/10.35940/ijeat.e2651.0610521.
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Biometrics refers to the automatic identification of a living person based on physiological or behavioural characteristics for authentication purpose. Among the existing biometric technologies are the face recognisation, fingerprint recognition, finger-geometry, hand geometry, iris recognition, vein recognition, voice recognition and signature recognition, Biometric method requires the physical presence of the person to be identified. This emphasizes its preference over the traditional method of identifying what you have such as, the use of password, a smartcard etc. Also, it potentially prevents unauthorized admittance to access control systems or fraudulent use of ATMs, Time Attendance Systems, cellular phones, smart cards, desktop PCs, Workstations, vehicles and computer networks. Biometric recognition systems offer greater security and convenience than traditional methods of personal recognition.
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Sabir, Azhin T. "Kinect-based human gait identifcation under different covariate factors." Innovaciencia Facultad de Ciencias Exactas Físicas y Naturales 6, no. 2 (December 28, 2018): 1–11. http://dx.doi.org/10.15649/2346075x.485.
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Introduction: Nowadays human gait identification/recognition is available in a variety of applications due to rapid advances in biometrics technology. This makes them easier to use for security and surveillance. Due to the rise in terrorist attacks during the last ten years research has focused on the biometric traits in these applications and they are now capable of recognising human beings from a distance. The main reason for my research interest in Gait biometrics is because it is unobtrusive and requires lower image/video quality compared to other biometric traits. Materials and Methods: In this paper we propose investigating Kinect-based gait recognition using non-standard gait sequences. This study examines different scenarios to highlight the challenges of non-standard gait sequences. Gait signatures are extracted from the 20 joint points of the human body using a Microsoft Kinect sensor. Results and Discussion: This feature is constructed by calculating the distances between each two joint points from the 20 joint points of the human body provided which is known as the Euclidean Distance Feature (EDF). The experiments are based on five scenarios, and a Linear Discriminant Classifier (LDC) is used to test the performance of the proposed method. Conclusions: The results of the experiments indicate that the proposed method outperforms previous work in all scenarios.
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Alhumyani, Hesham, Ghada M. El-Banby, Hala S. El-Sayed, Fathi E. Abd El-Samie, and Osama S. Faragallah. "Efficient Generation of Cancelable Face Templates Based on Quantum Image Hilbert Permutation." Electronics 11, no. 7 (March 26, 2022): 1040. http://dx.doi.org/10.3390/electronics11071040.
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The pivotal need to identify people requires efficient and robust schemes to guarantee high levels of personal information security. This paper introduces an encryption algorithm to generate cancelable face templates based on quantum image Hilbert permutation. The objective is to provide sufficient distortion of human facial biometrics to be stored in a database for authentication requirements through encryption. The strength of the proposed Cancelable Biometric (CB) scheme is guaranteed through the ability to generate cancelable face templates by performing the scrambling operation of the face biometrics after addition of a noise mask with a pre-specified variance and an initial seed. Generating the cancelable templates depends on a strategy with three basic steps: Initialization, Odd module, and Even module. Notably, the proposed scheme achieves high recognition rates based on the Area under the Receiver Operating Characteristic (AROC) curve, with a value up to 99.51%. Furthermore, comparisons with the state-of-the-art schemes for cancelable face recognition are performed to validate the proposed scheme.
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NAZAR, AKIF, ISSA TRAORÉ, and AHMED AWAD E. AHMED. "INVERSE BIOMETRICS FOR MOUSE DYNAMICS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (May 2008): 461–95. http://dx.doi.org/10.1142/s0218001408006363.
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Various techniques have been proposed in different literature to analyze biometric samples collected from individuals. However, not a lot of attention has been paid to the inverse problem, which consists of synthesizing artificial biometric samples that can be used for testing existing biometric systems or protecting them against forgeries. In this paper, we present a framework for mouse dynamics biometrics synthesis. Mouse dynamics biometric is a behavioral biometric technology, which allows user recognition based on the actions received from the mouse input device while interacting with a graphical user interface. The proposed inverse biometric model learns from random raw samples collected from real users and then creates synthetic mouse actions for fake users. The generated mouse actions have unique behavioral properties separate from the real mouse actions. This is shown through various comparisons of behavioral metrics as well as a Kolmogorov–Smirnov test. We also show through a two-fold cross-validation test that by submitting sample synthetic data to an existing mouse biometrics analysis model we achieve comparable performance results as when the model is applied to real mouse data.
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Peer, P., Ž. Emeršič, J. Bule, J. Žganec-Gros, and V. Štruc. "Strategies for Exploiting Independent Cloud Implementations of Biometric Experts in Multibiometric Scenarios." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/585139.
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Cloud computing represents one of the fastest growing areas of technology and offers a new computing model for various applications and services. This model is particularly interesting for the area of biometric recognition, where scalability, processing power, and storage requirements are becoming a bigger and bigger issue with each new generation of recognition technology. Next to the availability of computing resources, another important aspect of cloud computing with respect to biometrics is accessibility. Since biometric cloud services are easily accessible, it is possible to combine different existing implementations and design new multibiometric services that next to almost unlimited resources also offer superior recognition performance and, consequently, ensure improved security to its client applications. Unfortunately, the literature on the best strategies of how to combine existing implementations of cloud-based biometric experts into a multibiometric service is virtually nonexistent. In this paper, we try to close this gap and evaluate different strategies for combining existing biometric experts into a multibiometric cloud service. We analyze the (fusion) strategies from different perspectives such as performance gains, training complexity, or resource consumption and present results and findings important to software developers and other researchers working in the areas of biometrics and cloud computing. The analysis is conducted based on two biometric cloud services, which are also presented in the paper.
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Gaurav Melkani and Dr. Sunil Maggu. "Image-Based Face Detection and Recognition." International Journal for Modern Trends in Science and Technology 6, no. 12 (January 1, 2021): 466–70. http://dx.doi.org/10.46501/ijmtst061290.
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Face recognition from image or video is a popular topic in biometrics research. Many public places usually have surveillance cameras for video capture and these cameras have their significant value for security purpose. It is widely acknowledgedthatthefacerecognitionhaveplayedanimportant role in surveillance system as it doesn’t need the object’s cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. As human face is a dynamic object having high degree of variability in its appearance, that makes face detection a difficult problem in computer vision. In this field, accuracy and speed of identification is a mainissue. The goal of this paper is to evaluate various face detection and recognition methods, provide complete solution for image based face detection and recognition with higher accuracy, better response rate as an initial step for video surveillance. Solution is proposed based on performed tests on various face rich databases in terms of subjects, pose, emotions, race andlight.
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Alharbi, Abrar, Fahad Alharbi, and Eiji Kamioka. "Skeleton based gait recognition for long and baggy clothes." MATEC Web of Conferences 277 (2019): 03005. http://dx.doi.org/10.1051/matecconf/201927703005.
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Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%.
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Kim, Wan, Jong Min Song, and Kang Ryoung Park. "Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor." Sensors 18, no. 7 (July 15, 2018): 2296. http://dx.doi.org/10.3390/s18072296.
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Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.
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Kowalski, Marcin, and Krzysztof Mierzejewski. "Detection of 3D face masks with thermal infrared imaging and deep learning techniques." Photonics Letters of Poland 13, no. 2 (June 30, 2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.
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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural network-featured method for detecting presentation attacks. Full Text: PDF ReferencesS.R. Arashloo, J. Kittler, W. Christmas, "Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features", IEEE Trans. Inf. Forensics Secur. 10, 11 (2015). CrossRef A. Anjos, M.M. Chakka, S. Marcel, "Motion-based counter-measures to photo attacks inface recognition", IET Biometrics 3, 3 (2014). CrossRef M. Killioǧlu, M. Taşkiran, N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking", Proc. SAMI IEEE, (2017). CrossRef A. Asaduzzaman, A. Mummidi, M.F. Mridha, F.N. Sibai, "Improving facial recognition accuracy by applying liveness monitoring technique", Proc. ICAEE IEEE, (2015). CrossRef M. Kowalski, "A Study on Presentation Attack Detection in Thermal Infrared", Sensors 20, 14 (2020). CrossRef C. Galdi, et al, "PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study", IET Biometrics 9, 6 (2020). CrossRef D.A. Socolinsky, A. Selinger, J. Neuheisel, "Face recognition with visible and thermal infrared imagery", Comput. Vis Image Underst. 91, 1-2 (2003) CrossRef L. Sun, W. Huang, M. Wu, "TIR/VIS Correlation for Liveness Detection in Face Recognition", Proc. CAIP, (2011). CrossRef J. Seo, I. Chung, "Face Liveness Detection Using Thermal Face-CNN with External Knowledge", Symmetry 2019, 11, 3 (2019). CrossRef A. George, Z. Mostaani, D Geissenbuhler, et al., "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network", IEEE Trans. Inf. Forensics Secur. 15, (2020). CrossRef S. Ren, K. He, R. Girshick, J. Sun, "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition", Proc. CVPR IEEE 39, (2016). CrossRef K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", Proc. CVPR, (2016). CrossRef K. Mierzejewski, M. Mazurek, "A New Framework for Assessing Similarity Measure Impact on Classification Confidence Based on Probabilistic Record Linkage Model", Procedia Manufacturing 44, 245-252 (2020). CrossRef
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Algarni, Abeer D., Ghada El Banby, Sahar Ismail, Walid El-Shafai, Fathi E. Abd El-Samie, and Naglaa F. Soliman. "Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications." Entropy 22, no. 12 (November 30, 2020): 1361. http://dx.doi.org/10.3390/e22121361.
Full textAbstract:
The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.
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Herbadji, Abderrahmane, Noubeil Guermat, Lahcene Ziet, Zahid Akhtar, Mohamed Cheniti, and Djamel Herbadji. "Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies." Traitement du Signal 37, no. 6 (December 31, 2020): 889–97. http://dx.doi.org/10.18280/ts.370602.
Full textAbstract:
Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to higher person recognition accuracy; besides maintaining hygienic and pandemic concerns. Towards this aim, in this paper, an effective multi-biometric scheme for person authentication based on contactless fingerprint and palmprint selfies has been proposed. In this study, for simplicity and efficiency, three local descriptors, i.e., local phase quantization (LPQ), local Ternary patterns (LTP), and binarized statistical image features (BSIF), have been employed to extract salient features from contactless fingerprint and palmprint selfies. The score level fusion based multi-biometric system developed in this work combines the matching scores using two different fusion techniques, i.e., transformation based-rules like triangular norms and classifier based-rules like SVM. Experimental results on two publicly available databases (i.e., PolyU contactless to contact-based fingerprint database and IIT-Delhi touchless palmprint dataset) show that the proposed contactless multi-biometric selfie system can easily outperform uni-biometrics.