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Journal articles on the topic 'Unimodal Biometric Systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

S. Raju, A., and V. Udayashankara. "A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques." International Journal of Engineering & Technology 7, no. 4.36 (2018): 689. http://dx.doi.org/10.14419/ijet.v7i4.36.24224.

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Presently, a variety of biometric modalities are applied to perform human identification or user verification. Unimodal biometric systems (UBS) is a technique which guarantees authentication information by processing distinctive characteristic sequences and these are fetched out from individuals. However, the performance of unimodal biometric systems restricted in terms of susceptibility to spoof attacks, non-universality, large intra-user variations, and noise in sensed data. The Multimodal biometric systems defeat various limitations of unimodal biometric systems as the sources of different

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Omar, Bayan. "Fusion Method with Mean-discrete Algorithm in Feature level for Identical twins Identification." UHD Journal of Science and Technology 4, no. 2 (2020): 141. http://dx.doi.org/10.21928/uhdjst.v4n2y2020.pp141-150.

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The study on twins is an important form of study in the forensic and biometrics field as twins share similar genetic traits. A biometric is one of the common types of pattern recognition which acquires biometric data from a person. From these data, a feature is established and extracted where these features can be used to identify individual. Exiting works in biometric identification concentrate on unimodal biometric identification. The high similarity in a pair of twin’s biometric may lead to miss performance. Hence, due to their great accurateness, multimodal biometric systems have become mo

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Paul, Padma P., and Marina L. Gavrilova. "A Novel Cross Folding Algorithm for Multimodal Cancelable Biometrics." International Journal of Software Science and Computational Intelligence 4, no. 3 (2012): 20–37. http://dx.doi.org/10.4018/jssci.2012070102.

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Multimodal biometric systems have emerged as highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. However, one major issue pertinent to unimodal system remains, which has to do with actual biometric characteristics of users being permanent and their number being limited. Thus, if a user’s biometric is compromised, it might be impossible or highly difficult to replace it in a particular system. The concept of cancelable biometric or cancelability is to trans

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Boucetta, Aldjia, and Kamal Eddine Melkemi. "Score Level Fusion of Palmprint, Face and Iris Using Adaptive PSO." International Journal of Applied Metaheuristic Computing 10, no. 3 (2019): 175–94. http://dx.doi.org/10.4018/ijamc.2019070109.

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Systems that use unimodal biometrics often suffer from various drawbacks such as noise in sensed data, variations that are due to intra class, nonuniversality, spoof attacks, restricted degrees of freedom and high error rates. These limitations can be solved effectively by combining two or more biometric modalities. In this article, a multimodal biometric fusion system is presented that combines palmprint, face and iris traits. The biometric fusion is performed at the score level in order to improve the accuracy of the system. Scores obtained from the three classifiers are fused using adaptive

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Damousis, I. G., and S. Argyropoulos. "Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/242401.

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We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs), Artificial Neural Networks (ANNs), Fuzzy Expert Systems (FESs), and Support Vector Machines (SVMs). The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and th

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Oloyede, Muhtahir O., and Gerhard P. Hancke. "Unimodal and Multimodal Biometric Sensing Systems: A Review." IEEE Access 4 (2016): 7532–55. http://dx.doi.org/10.1109/access.2016.2614720.

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Sujana, S., and V. S. K. Reddy. "Comparison of levels and fusion approaches for multimodal biometrics." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 791. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp791-801.

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The biometric-based authentication system occupies maximal space in the field of security administration. Biometric applications are swiftly accelerating in day-to-day life such as computer login, smart homes, online banking, hospitals, border areas, industries, forensics, e-voting attendance system and investigation of crime. A reliable and accurate recognition body can be achieved with multimodal biometric methodologies. In this paper, we discuss starting with an introduction to biometric systems followed by their classification, and advantages as well as disadvantages. In today’s world, mos

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Et. al., Meena Tiwari. "Enhancing the Accuracy of Multimodal Biometric Systems." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 5142–49. http://dx.doi.org/10.17762/turcomat.v12i3.2063.

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: Biometric acknowledgment frameworks have progressed altogether in the most recent decade and their utilization in explicit applications will increment sooner rather than later. The capacity to direct important correlations and evaluations will be urgent to fruitful organization and expanding biometric selection. Indeed, even the best methodology and unimodal biometric frameworks couldn't completely address the issue of exactness and execution as far as their bogus acknowledge rate (FAR) and bogus oddball rate (FRR). In spite of the fact that multimodal biometric frameworks had the option to

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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 (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 statist

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Gawande, Ujwalla, Mukesh Zaveri, and Avichal Kapur. "A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/515918.

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Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented

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Arunarani, S., and R. Gobinath. "A survey on multimodal biometrics for human authentication." International Journal of Engineering & Technology 7, no. 3.3 (2018): 273. http://dx.doi.org/10.14419/ijet.v7i2.33.14167.

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Authentication process identifies an individual to get an endorsed access by entering their login credentials. The inconvenience with this method is the user must remember the keywords, and the passwords can be predicted or if it is hard to guess it will be cracked through brute force. Due to this fault, this method is lack of integrity. Biometrics sample recognize a person based on his behavioral or physiological char-acteristics. Unimodal biometric systems have to resist with a different types of problems such as inconsistent data, intra-class variations, deceit attacks and high error rates.

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Singh, Law Kumar, Munish Khanna, Shankar Thawkar, and Jagadeesh Gopal. "Robustness for Authentication of the Human Using Face, Ear, and Gait Multimodal Biometric System." International Journal of Information System Modeling and Design 12, no. 1 (2021): 39–72. http://dx.doi.org/10.4018/ijismd.2021010103.

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Biometrics is the science that deals with personal human physiological and behavioral characteristics such as fingerprints, handprints, iris, voice, face recognition, signature recognition, ear recognition, and gait recognition. Recognition using a single trait has several problems and multimodal biometrics system is one of the solutions. In this work, the novel and imperative biometric feature gait is fused with face and ear biometric features for authentication and to overcome problems of the unimodal biometric recognition system. The authors have also applied various normalization methods t

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Wu, Zhendong, Jiajia Yang, Jianwu Zhang, and Hengli Yue. "Multibiometric Fusion Authentication in Wireless Multimedia Environment Using Dynamic Bayesian Method." Security and Communication Networks 2018 (November 18, 2018): 1–12. http://dx.doi.org/10.1155/2018/5783976.

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Single biometric method has been widely used in the field of wireless multimedia authentication. However, it is vulnerable to spoofing and limited accuracy. To tackle this challenge, in this paper, we propose a multimodal fusion method for fingerprint and voiceprint by using a dynamic Bayesian method, which takes full advantage of the feature specificity extracted by a single biometrics project and authenticates users at the decision-making level. We demonstrate that this method can be extended to more modal biometric authentication and can achieve flexible accuracy of the authentication. The

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Afzal, Maria, Mohd Abdul Ahad, and Jyotsana Grover. "Score Level Fusion of Multispectral Palmprint with Triangular Conorm." International Journal of Emerging Research in Management and Technology 6, no. 6 (2018): 88. http://dx.doi.org/10.23956/ijermt.v6i6.251.

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Biometricplay vigorous role in the authentication of user by using his/her physical body traits. Unimodal biometric system uses single body traits and multimodal systems use multiple body traits. Multimodal biometric system have overcome the disadvantages that has occurred in unimodal systems. In this paper we are fusing the different spectral bandsof palm print (Red, Green and Blue) using T-conorm operators like Hamacher, Frank, Probabilistic and Scheiwer & Sklar. Experiment Results suggest that Scheiwer & Sklar gives the best results. Experimental Results ascertain that the proposed

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Herbadji, Abderrahmane, Zahid Akhtar, Kamran Siddique, et al. "Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition." Symmetry 12, no. 3 (2020): 444. http://dx.doi.org/10.3390/sym12030444.

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Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can

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Conti, Vincenzo, Carmelo Militello, Filippo Sorbello, and Salvatore Vitabile. "Biometric sensors rapid prototyping on field-programmable gate arrays." Knowledge Engineering Review 30, no. 2 (2015): 201–19. http://dx.doi.org/10.1017/s0269888914000307.

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AbstractBiometric user authentication in large-scale distributed systems involves passive scanners and networked workstations and databases for user data acquisition, processing, and encryption. Unfortunately, traditional biometric authentication systems are prone to several attacks, such as Replay Attacks, Communication Attacks, and Database Attacks. Embedded biometric sensors overcome security limits of conventional software recognition systems, hiding its common attack points. The availability of mature reconfigurable hardware technology, such as field-programmable gate arrays, allows the d

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Roy, Kaushik, Brian O'Connor, Foysal Ahmad, and Mohamed S. Kamel. "Multibiometric System Using Level Set, Modified LBP and Random Forest." International Journal of Image and Graphics 14, no. 03 (2014): 1450013. http://dx.doi.org/10.1142/s0219467814500132.

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Multibiometric systems alleviate some of the shortcomings possessed by the unimodal biometrics and provide better recognition performance. This paper presents a multibiometric system that integrates the iris and face features based on the fusion at the feature level. The proposed multibiometric system has three novelties as compared to the previous works. First, distance regularized level-set evolution (DRLSE) technique is utilized to localize the iris and pupil boundary from an iris image. The DRLSE maintains the regularity of the level set function intrinsically during the curve evolution pr

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Alay, Nada, and Heyam H. Al-Baity. "Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits." Sensors 20, no. 19 (2020): 5523. http://dx.doi.org/10.3390/s20195523.

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With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is b

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Taouche, Chérif, and Hacene Belhadef. "Multimodal biometric system combining left and right palmprints." Information Discovery and Delivery 48, no. 1 (2019): 2–13. http://dx.doi.org/10.1108/idd-01-2019-0011.

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Purpose Palmprint recognition is a very interesting and promising area of research. Much work has already been done in this area, but much more needs to be done to make the systems more efficient. In this paper, a multimodal biometrics system based on fusion of left and right palmprints of a person is proposed to overcome limitations of unimodal systems. Design/methodology/approach Features are extracted using some proposed multi-block local descriptors in addition to MBLBP. Fusion of extracted features is done at feature level by a simple concatenation of feature vectors. Then, feature select

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Moreno-Rodriguez, Juan Carlos, Juan Carlos Atenco-Vazquez, Juan Manuel Ramirez-Cortes, Rene Arechiga-Martinez, Pilar Gomez-Gil, and Rigoberto Fonseca-Delgado. "BIOMEX-DB: A Cognitive Audiovisual Dataset for Unimodal and Multimodal Biometric Systems." IEEE Access 9 (2021): 111267–76. http://dx.doi.org/10.1109/access.2021.3100035.

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T., Vijayakumar. "Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature." Journal of Innovative Image Processing 3, no. 2 (2021): 131–43. http://dx.doi.org/10.36548/jiip.2021.2.005.

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Biometric identification technology is widely utilized in our everyday lives as a result of the rising need for information security and safety laws throughout the world. In this aspect, multimodal biometric recognition (MBR) has gained significant research attention due to its ability to overcome several important constraints in unimodal biometric systems. Henceforth, this research article utilizes multiple features such as an iris, face, finger vein, and palm print for obtaining the highest accuracy to identify the exact person. The utilization of multiple features from the person improves t

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Moutafis, Panagiotis, and Ioannis A. Kakadiaris. "Can We Do Better in Unimodal Biometric Systems? A Rank-Based Score Normalization Framework." IEEE Transactions on Cybernetics 45, no. 12 (2015): 2654–67. http://dx.doi.org/10.1109/tcyb.2014.2379174.

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Xiong, Qi, Xinman Zhang, Xuebin Xu, and Shaobo He. "A Modified Chaotic Binary Particle Swarm Optimization Scheme and Its Application in Face-Iris Multimodal Biometric Identification." Electronics 10, no. 2 (2021): 217. http://dx.doi.org/10.3390/electronics10020217.

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In order to improve the recognition rate of the biometric identification system, the features of each unimodal biometric are often combined in a certain way. However, there are some mutually exclusive redundant features in those combined features, which will degrade the identification performance. To solve this problem, this paper proposes a novel multimodal biometric identification system for face-iris recognition.It is based on binary particle swarm optimization. The face features are extracted by 2D Log-Gabor and Curvelet transform, while iris features are extracted by Curvelet transform. I

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Mwema, Joseph, Stephen Kimani, and Michael Kimwele. "A Conceptual Technique for Deriving Encryption Keys from Fingerprints to Secure Fingerprint Templates in Unimodal Biometric Systems." International Journal of Computer Applications 118, no. 9 (2015): 18–30. http://dx.doi.org/10.5120/20773-3252.

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Zhang, Xiaomei, Pengming Zhang, and Haomin Hu. "Multimodal Continuous User Authentication on Mobile Devices via Interaction Patterns." Wireless Communications and Mobile Computing 2021 (August 17, 2021): 1–15. http://dx.doi.org/10.1155/2021/5677978.

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Behavior-based continuous authentication is an increasingly popular methodology that utilizes behavior modeling and sensing for authentication and account access authorization. As an appearing behavioral biometric, user interaction patterns with mobile devices focus on verifying their identity in terms of their features or operating styles while interacting with devices. However, unimodal continuous authentication schemes, which are on the basis of a single source of interaction information, can only deal with a particular action or scenario. Hence, multimodal systems should be taken to suit f

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FENG, XINHUA, XIAOQING DING, YOUSHOU WU, and PATRICK S. P. WANG. "CLASSIFIER COMBINATION AND ITS APPLICATION IN IRIS RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (2008): 617–38. http://dx.doi.org/10.1142/s0218001408006314.

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Classifier combination is an effective method to improve the recognition accuracy of a biometric system. It has been applied to many practical biometric systems and achieved excellent performance. However, there is little literature involving theoretical analysis on the effectiveness of classifier combination. In this paper, we investigate classifiers combined with the max and min rules. In particular, we compute the recognition performance of each combined classifier, and illustrate the condition in which the combined classifier outperforms the original unimodal classifier. We focus our study

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Cherrat, El mehdi, Rachid Alaoui, and Hassane Bouzahir. "Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images." PeerJ Computer Science 6 (January 6, 2020): e248. http://dx.doi.org/10.7717/peerj-cs.248.

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In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional neural network (CNN), Softmax and Random forest (RF) classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied

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Lopes Silva, Pedro, Eduardo Luz, Gladston Moreira, Lauro Moraes, and David Menotti. "ChimericalDataset Creation Protocol Based on Doddington Zoo: A Biometric Application with Face, Eye, and ECG." Sensors 19, no. 13 (2019): 2968. http://dx.doi.org/10.3390/s19132968.

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Multimodal systems are a workaround to enhance the robustness and effectiveness of biometric systems. A proper multimodal dataset is of the utmost importance to build such systems. The literature presents some multimodal datasets, although, to the best of our knowledge, there are no previous studies combining face, iris/eye, and vital signals such as the Electrocardiogram (ECG). Moreover, there is no methodology to guide the construction and evaluation of a chimeric dataset. Taking that fact into account, we propose to create a chimeric dataset from three modalities in this work: ECG, eye, and

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Alqaralleh, Esraa, and Önsen Toygar. "Ear Recognition Based on Fusion of Ear and Tragus Under Different Challenges." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 09 (2018): 1856009. http://dx.doi.org/10.1142/s0218001418560098.

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This paper proposes a 2D ear recognition approach that is based on the fusion of ear and tragus using score-level fusion strategy. An attempt to overcome the effect of partial occlusion, pose variation and weak illumination challenges is done since the accuracy of ear recognition may be reduced if one or more of these challenges are available. In this study, the effect of the aforementioned challenges is estimated separately, and many samples of ear that are affected by two different challenges concurrently are also considered. The tragus is used as a biometric trait because it is often free f

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Farmanbar, Mina, and Önsen Toygar. "A Hybrid Approach for Person Identification Using Palmprint and Face Biometrics." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (2015): 1556009. http://dx.doi.org/10.1142/s0218001415560091.

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This paper proposes hybrid approaches based on both feature level and score level fusion strategies to provide a robust recognition system against the distortions of individual modalities. In order to compare the proposed schemes, a virtual multimodal database is formed from FERET face and PolyU palmprint databases. The proposed hybrid systems concatenate features extracted by local and global feature extraction methods such as Local Binary Patterns, Log Gabor, Principal Component Analysis and Linear Discriminant Analysis. Match score level fusion is performed in order to show the effectivenes

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"Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions." International Journal of Advanced Trends in Computer Science and Engineering 10, no. 3 (2021): 1827–31. http://dx.doi.org/10.30534/ijatcse/2021/471032021.

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Authentication is a very important aspect of computer security. Most systems employ strategies such as Password-based authentication, Multi-factor authentication, Certificate based authentication which are accompanied with a lot ofchallenges. To address this issue, most security systems have introduced the use of biometrics for authentication. Unimodal biometrics systems have many limitations regarding performance and accuracy. The use of Multimodal biometrics systems for authentication is recently attracting the attention of researchers due to its capacity to overcome most of the drawbacks of

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"Hybridization for Classification and Identification of Individuals using Multimodal Biometric Systems." International Journal of Engineering and Advanced Technology 9, no. 4 (2020): 2126–32. http://dx.doi.org/10.35940/ijeat.d7249.049420.

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Biometric recognition systems use certain human characteristics such as voice, facial features, fingerprint, iris or hand geometry to identify an individual or verify their identity. These systems have been developed individually for each of these biometric modalities until they achieve remarkable levels of performance. Biometrics is a measure of biological characteristics for the identification or authentication of an individual based on some of its characteristics. Although biometric recognition techniques promise to be very effective, At present, we can not guarantee an excellent identifica

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Vandana and Navdeep Kaur. "Analytical Review of Biometric Technology Employing Vivid Modalities." International Journal of Image and Graphics, April 26, 2021, 2250004. http://dx.doi.org/10.1142/s0219467822500048.

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The digitalization has been challenged with the security and privacy aspects in each and every field. In addition to numerous authentication methods, biometrics has been popularized as it relies on one’s individual behavioral and physical characters. In this context, numerous unimodal and multimodal biometrics have been proposed and tested in the last decade. In this paper, authors have presented a comprehensive survey of the existing biometric systems while highlighting their respective challenges, advantage and limitations. The paper also discusses the present biometric technology market val

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"Convolutional Neural Network Based Multimodal Biometric Human Authentication using Face, Palm Veins and Fingerprint." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (2020): 771–77. http://dx.doi.org/10.35940/ijitee.c8467.019320.

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security access control systems and forensic applications. Performance of conventional unimodal biometric systems is generally suffered due to the noisy data, non universality and intolerable error rate. In propose system, multi layer Convolutional Neural Network (CNN) is applied to multimodal biometric human authentication using face, palm vein and fingerprints to increase the robustness of system. For the classification linear Support Vector Machine classifier is used. For the evaluation of system self developed face, palm vein and fingerprint database having 4,500 images are used. The perfo

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"Multimodal Biometric Recognition: Fusion of Modified Adaptive Bilinear Interpolation Data Samples of Face and Signature using Local Binary Pattern Features." International Journal of Engineering and Advanced Technology 9, no. 3 (2020): 3111–20. http://dx.doi.org/10.35940/ijeat.c6117.029320.

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Biometric based authentication systems use particular person characteristics which might be based on either behavior like voice, signature etc. or body structure like face, iris, palm print, fingerprint, etc. The performance of any unimodal biometric arrangement is depending on elements like surroundings, atmosphere, and sensor precision. Also, there are numerous trait unique demanding situations which include pose, expression, growing old and so forth for face reputation, occlusion and acquisition related problems for iris and terrible high-quality and social popularity related troubles for f

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Annavarapu, Ambika, Surekha Borra, and Rohit Thanki. "Progression in Biometric Recognition Systems and its Security." Recent Patents on Engineering 14 (September 18, 2020). http://dx.doi.org/10.2174/1872212114999200918122905.

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: The security of information is the major problem faced in today’s digital environment. Biometric Systems helps in achieving high–end security at precise performance requirements for various applications. This paper surveys the present multimodal and unimodal biometric based authentication systems which are in use, and their future possibilities. The authentication techniques that can identify the individual using unique features of the human such as face, fingerprint, DNA, speech and iris are discussed. Different recognition systems for new biometric traits such as voice, gait, and lip momen

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"Verification of Biometric Traits using Deep Learning." International Journal of Innovative Technology and Exploring Engineering 8, no. 10S (2019): 452–59. http://dx.doi.org/10.35940/ijitee.j1083.08810s19.

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Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems including non-universality, noise, population coverage, vulnerability and intra-class variability for verification, authentication and identification of an individual. In this paper, the impact of deep learning in the field of biometrics is investigated where supervised learning is primarily involved in identifying biometric traits using Graphical User Interface. The trained deep learning system proposed is calle

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"Multi-Modal Authentication using Machine Learning Algorithm." International Journal of Engineering and Advanced Technology 8, no. 6S3 (2019): 1151–55. http://dx.doi.org/10.35940/ijeat.f1192.0986s319.

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In the present era of information technology, there is a need to implement verification and approval strategies for security of resources. Whereas, there are number of approaches to demonstrate validation and approval, yet the biometric verification beat every other method. At first, biometrics began off with straightforward unimodal framework, the higher requirement for security had offered ascend to a prevalent framework known as multimodal verification framework. Multimodal verification confirmation has pulled in compelling interest, on account of its hugeness towards the constant applicati

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"Novel Biometric Fusion System using GA-PSO and ANN." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (2020): 3122–29. http://dx.doi.org/10.35940/ijitee.d1301.029420.

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Technology advancements have led to the emergence of biometrics as the most relevant future authentication technology. On practical grounds, unimodal biometric authentication systems have inevitable momentous limitations due to varied data quality and noise levels. The paper aims at investigating fusion of face and fingerprint biometric characteristics to achieve a high level personal authentication system. In the fusion strategy face features are extracted using Scale-Invariant Feature Transform (SIFT) algorithm and fingerprint features are extracted using minutiae feature extraction. These e

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Choudhary, Swati K., and Ameya K. Naik. "Multimodal Biometric-Based Authentication with Secured Templates." International Journal of Image and Graphics, December 23, 2020, 2150018. http://dx.doi.org/10.1142/s0219467821500182.

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This paper proposes a multimodal biometric based authentication (verification and identification) with secured templates. Multimodal biometric systems provide improved authentication rate over unimodal systems at the cost of increased concern for memory requirement and template security. The proposed framework performs person authentication using face and fingerprint. Biometric templates are protected by hiding fingerprint into face at secret locations, through blind and key-based watermarking. Face features are extracted from approximation sub-band of Discrete Wavelet Transform, which reduces

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"A Robust Multimodel Biometric Crypto System." International Journal of Recent Technology and Engineering 8, no. 2S8 (2019): 1953–61. http://dx.doi.org/10.35940/ijrte.b1206.0882s819.

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Biometric based authentication has several advantages over traditional password or PIN based authentication process because biometric is consists of physical or behavioural characteristics i.e fingerprint, face, Finger Knuckle Print (FKP), iris, voice etc. Unimodal biometric system h as some drawbacks i.e non universality, inter-class variation, intra-class variation; system can be circumvented by the skilled imposter etc. These drawbacks can overcome by multimodal biometric system as it combines more than one modality for authentication. When multimodal system combined with cryptography it ma

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Alazawi, Sundos Abdulameer, Huda Abdulaaliabdulbaqi, and Yasmin Makki Mohialden. "New Authentication Model for Multimodal Biometrics Based on Shape Features Vectors." Journal of Southwest Jiaotong University 54, no. 6 (2019). http://dx.doi.org/10.35741/issn.0258-2724.54.6.18.

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Biometrics is the science and technology dealing with the measurement and analysis of the biological features of the human body. The analysis is based on comparing the value of certain measured features with the form features in the database. Unimodal Biometric Systems have many limitations regarding precision in the identification/authentication of personal data. To accurately identify a person, a multimodal biometrics system such as combining face and fingerprint characteristic is used. Many such multi-biometrics fusion possibilities exist that can be utilized as an authentication system. In

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"New Aadhaar with Multimodal Biometric Model-Based E-Voting System with Dynamic Hybrid IANFIS-PSO." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (2020): 1416–21. http://dx.doi.org/10.35940/ijitee.f4529.049620.

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This work deals with the E-voting system with a biometric concept that will make the voting system smart, secure and easy to vote which can be linked with Aadhaar card. While the process of doing the Aadhaar enrolment process Authorities gathered information of fingerprints and iris of every character and this whole fact of every person persists in the Indian government database. However these two biometric is not enough for the voter authentication process, besides improving the recognition rate, combining multimodal biometric modalities might be more appropriate for E-voting applications. If

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"Multimodal Eye Biometric System Based on Contour Based E-CNN and Multi Algorithmic Feature Extraction Using SVBF Matching." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (2019): 417–23. http://dx.doi.org/10.35940/ijitee.i7729.078919.

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Recent advancement in biometric system prefer multimodal biometric system instead of single biometric system to overcome challenges faced by unimodal biometric system such as intra class variation, noise sensitivity, non universality, spoofing attack, etc. Most of the existing iris biometric systems are dependent on ideal condition which needs user cooperation during image acquisition with help of NIR camera to avoid noise. Such system performance significantly degrades when images are taken under visible light without user cooperation called unconstrained environment. Proposed multi modal eye

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Jooda, J. O., A. O. Oke, E. O. Omidiora, and O. T. Adedeji. "Fingerprint Intramodal Biometric System Based on ABC Feature Fusion." Asian Journal of Research in Computer Science, August 13, 2021, 1–10. http://dx.doi.org/10.9734/ajrcos/2021/v11i230256.

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Unimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system's classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful informatio

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