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Journal articles on the topic 'Biometric performance'
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1
Bhirud, Priya, and Nandana Prabhu. "Performance Evaluation of Filters of Discrete Wavelet Transforms for Biometrics." International Journal of Informatics and Communication Technology (IJ-ICT) 3, no. 2 (August 1, 2014): 97. http://dx.doi.org/10.11591/ijict.v3i2.pp97-102.
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<p>Biometrics associated with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Commonly used biometric features are facial features, fingerprints, voice, facial thermo grams, iris, posture/gait, palm print, hand geometry etc. Compared with other biometric characteristics iris is the most stable and hence the most reliable biometric characteristic over the period of a lifetime. This proposed work provides comparative study of various filters of Wavelet Transforms in terms of size and PSNR of images<em>.</em></p>
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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 (December 9, 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 biometrics typically compensate for the inherent limitations of one another. The objective of this article is to analyze various methods of information fusion for biometrics, and summarize, to conclude with direction on future research proficiency in a multimodal biometric system using ECG, Fingerprint and Face features. This paper is furnished as a ready reckoner for those researchers, who wish to persue their work in the area of biometrics.
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Sabharwal, Munish. "Multi-Modal Biometric Authentication and Secure Transaction Operation Framework for E-Banking." International Journal of Business Data Communications and Networking 13, no. 1 (January 2017): 102–16. http://dx.doi.org/10.4018/ijbdcn.2017010109.
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The rationale of the research work is to suggest a multi-modal biometric authentication and secure transaction operation framework for E-Banking. The literature survey identifies the various types of E-Banking Channels available as on-date, the various types of biometric technologies available as on-date as well the significant metrics affecting their performance while deploying them in various different e-banking channels. The performance analysis of various types of biometric technologies based on significant metrics for Biometrics Implementation further identifies the currently implementable biometric technologies for the various different e-banking channels. Subsequently a requirement analysis of potential e-banking channels is followed by System Suitability Analysis to identify which multi-biometrics and support mechanisms are suitable for particular e-banking channels. The final conclusion suggests a viable multi-modal biometric authentication and secure transaction operation framework for various e-banking channels.
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Rasiah, Devinaga, and Yuen Yee Yen. "User acceptance of ATM biometric authentication." Global Journal of Computer Sciences: Theory and Research 10, no. 1 (April 30, 2020): 11–17. http://dx.doi.org/10.18844/gjcs.v10i1.4748.
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Malaysia is amongst one of the first countries in the world to use biometrics as a standard in its identification cards and automatic teller machines (ATMs). Currently, there is a lack of studies in Malaysia and around the world that studied the user acceptance of ATM biometric authentication technology. All previous studies tended to overlook the user acceptance of ATM biometrics authentication which is an increasingly important mechanism to verify user identity in banking industry. Therefore, this study is carried out to examine the key factors of user acceptance of ATM biometric authentication technology in Malaysian banks. The target population of this study was customers of commercial banks in the central region of Malaysia. The participants were randomly selected. The questionnaire was distributed to 200 bank customers who have at least 1-year experience in using ATM biometric authentication. The items in the questionnaire were derived from the extensive survey of relevant literature and oral interview. The findings show that both the performance expectancy and social influence have significant effects on user adoption of ATM biometric authentication.
Keywords: ATM, behavioural intention to use, biometrics, performance expectancy, social influence.
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Punithavathi, P., and S. Geetha. "Random Projection-based Cancelable Template Generation for Sparsely Distributed Biometric Patterns." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (September 1, 2017): 877. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp877-886.
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Cancelable biometrics, a template transformation approach, attempts to provide robustness for authentication services based on biometrics. Several biometric template protection techniques represent the biometric information in binary form as it provides benefits in matching and storage. In this context, it becomes clear that often such transformed binary representations can be easily compromised and breached. In this paper, we propose an efficient non-invertible template transformation approach using random projection technique and Discrete Fourier transformation to shield the binary biometric representations. The cancelable fingerprint templates designed by the proposed technique meets the requirements of revocability, diversity, non-invertibility and performance. The matching performance of the cancelable fingerprint templates generated using proposed technique, have improved when compared with the state-of-art methods.
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Moujahdi, Chouaib, George Bebis, Sanaa Ghouzali, Mounia Mikram, and Mohammed Rziza. "Biometric Template Protection Using Spiral Cube: Performance and Security Analysis." International Journal on Artificial Intelligence Tools 25, no. 01 (February 2016): 1550027. http://dx.doi.org/10.1142/s021821301550027x.
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Personal authentication systems based on biometrics have given rise to new problems and challenges related to the protection of personal data, issues of less concern in traditional authentication systems. The irrevocability of biometric templates makes biometric systems very vulnerable to several attacks. In this paper we present a new approach for biometric template protection. Our objective is to build a non-invertible transformation, based on random projection, which meets the requirements of revocability, diversity, security and performance. In this context, we use the chaotic behavior of logistic map to build the projection vectors using a methodology that makes the construction of the projection matrix depend on the biometric template and its identity. The proposed approach has been evaluated and compared with Biohashing and BioPhasor using a rigorous security analysis. Our extensive experimental results using several databases (e.g., face, finger-knuckle and iris), show that the proposed technique has the ability to preserve and increase the performance of protected systems. Moreover, it is demonstrated that the security of the proposed approach is sufficiently robust to possible attacks keeping an acceptable balance between discrimination, diversity and non-invertibility.
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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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Barzut, Srđan, Milan Milosavljević, Saša Adamović, Muzafer Saračević, Nemanja Maček, and Milan Gnjatović. "A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks." Mathematics 9, no. 7 (March 28, 2021): 730. http://dx.doi.org/10.3390/math9070730.
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Modern access controls employ biometrics as a means of authentication to a great extent. For example, biometrics is used as an authentication mechanism implemented on commercial devices such as smartphones and laptops. This paper presents a fingerprint biometric cryptosystem based on the fuzzy commitment scheme and convolutional neural networks. One of its main contributions is a novel approach to automatic discretization of fingerprint texture descriptors, entirely based on a convolutional neural network, and designed to generate fixed-length templates. By converting templates into the binary domain, we developed the biometric cryptosystem that can be used in key-release systems or as a template protection mechanism in fingerprint matching biometric systems. The problem of biometric data variability is marginalized by applying the secure block-level Bose–Chaudhuri–Hocquenghem error correction codes, resistant to statistical-based attacks. The evaluation shows significant performance gains when compared to other texture-based fingerprint matching and biometric cryptosystems.
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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 (July 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 transform a biometric data or feature into a new one so that the stored biometric template can be easily changed in a biometric security system. In this paper, the authors present a novel solution for cancelable biometrics in a multimodal system. They develop a new cancelable biometric template generation algorithm using random projection and transformation-based feature extraction and selection. Performance of the proposed algorithm is validated on a virtual multi-modal face and ear database.
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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 (December 27, 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 more favored than unimodal biometric systems in identical twins identification. However, these systems are highly complex. We proposed Mean-Discrete feature based fusion algorithm for Kurdish handwriting and fingerprint for identical twins detection. Its viability and advantage over the unimodal biometric systems are highlighted. This paper employed 800 images from 50 pairs of identical twins from Kurdistan Region to carry out the experiment.
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Taha, Mohammed A., and Hanaa M. Ahmed. "A fuzzy vault development based on iris images." EUREKA: Physics and Engineering, no. 5 (September 13, 2021): 3–12. http://dx.doi.org/10.21303/2461-4262.2021.001997.
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Biometric systems gather information from the person's biometric attributes, used extensively to authorize the individuals. Due to the obvious convenience of using specific individual traits such as face, fingerprints, palm veins, and irises, biometric authentication is becoming more common. In particular, Iris systems are in high demand for high-assurance applications, because they contain a broad feature set and remain stable. Authentication methods based on iris biometrics are now commonly used in a variety of fields. This is due to the fact that iris biometric authentication is both safer and more comfortable than conventional passwords. Template Security is a major concern in biometric systems. The template security mechanism ensures reusable, permanent, and un-linkable models. The Fuzzy Vault strategy is one of the most popular security schemes for Template protection.
Fuzzy vault has demonstrated to be an effective protection method but lacks revocability and security attacks. This article introduced an improved fuzzy vault system. The improved fuzzy vault system was introduced, which uses more than one key to protect biometric data. Different keys make the search space more detailed. The additional key was used to encrypt vault data, which stopped the intruder from accessing the information on the person's biometry. The system was tested using CASIA.v1 and IITD.v1 datasets, and findings showed that the system ensures the protection and authentication of the iris templates without compromising performance. The proposed modification gave a 0.0 % False Accepted Rate (FAR) for both the dataset and False Rejected Rate (FRR), 0.14 % for CASIA v1 and 0.12 % for ITTD v1 False Rejected rate
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Chaudhary, Sheetal, Rajender Nath, Chander Kant, and Surya Kant. "Approach for Protection of Iris Template using Cancelable Biometrics." International Journal of Sensors, Wireless Communications and Control 9, no. 1 (July 15, 2019): 144–52. http://dx.doi.org/10.2174/2210327908666181011143736.
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Background and Objective: The most important issue concerning the security of biometric authentication systems is protection of biometric templates. This is because once the biometric template being attacked, it cannot be canceled and reissued. Thus, the intruder could avail the facilities that are meant only for the genuine user just bypassing the enrollment phase. Methods: To protect the biometric templates from attacks, it is advantageous to modify them before storing in the databases through some cancelable and non-invertible transformations. Hence, an approach based on cancelable biometrics is proposed in this paper for providing security and privacy to biometric templates. It uses left iris and right iris as input biometric traits. Different experiments are carried out to authenticate the proposed approach. Results and Conclusion: It satisfies all template protection requirements and expected to show good recognition performance without degrading it.
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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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Kim, Hanvit, Haena Kim, Se Chun, Jae-Hwan Kang, Ian Oakley, Youryang Lee, Jun Ryu, et al. "A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors." Sensors 18, no. 8 (August 20, 2018): 2738. http://dx.doi.org/10.3390/s18082738.
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Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD . 1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD . 1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication.
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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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Karthi, G., and M. Ezhilarasan. "Multi biometric Template Protection using Hybrid Technique." International Journal of Engineering & Technology 7, no. 4 (September 24, 2018): 2609. http://dx.doi.org/10.14419/ijet.v7i4.11485.
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Recently, multi-biometrics system has been the important identification system for providing authentication mechanism. In this pa-per, the multi-biometric recognition system uses multiple traits (face, iris and fingerprint) for authentication. The features are extracted from the traits and feature level fusion technique is applied to the individual features traits to form a fused feature. Protection of these biometrics features against various attacks points is an important concern for authentication process. One such attack is the modification of stored template, which largely affects the performance of biometric recognition system. This paper addresses this concern, by apply-ing template protection algorithm to the biometric features. An improved hybrid template protection algorithm is proposed to protect the biometric template.The experimental results show that the proposed algorithm works better than the existing algorithms available. The proposed algorithm provides better protection to the template. Further, attacks are performed on the proposed system which provide strong resistant against the attacks.
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Chai, Tong-Yuen, Bok-Min Goi, Yong-Haur Tay, and and Zhe Jin. "A New Design for Alignment-Free Chaffed Cancelable Iris Key Binding Scheme." Symmetry 11, no. 2 (February 1, 2019): 164. http://dx.doi.org/10.3390/sym11020164.
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Iris has been found to be unique and consistent over time despite its random nature. Unprotected biometric (iris) template raises concerns in security and privacy, as numerous large-scale iris recognition projects have been deployed worldwide—for instance, susceptibility to attacks, cumbersome renewability, and cross-matching. Template protection schemes from biometric cryptosystems and cancelable biometrics are expected to restore the confidence in biometrics regarding data privacy, given the great advancement in recent years. However, a majority of the biometric template protection schemes have uncertainties in guaranteeing criteria such as unlinkability, irreversibility, and revocability, while maintaining significant performance. Fuzzy commitment, a theoretically secure biometric key binding scheme, is vulnerable due to the inherent dependency of the biometric features and its reliance on error correction code (ECC). In this paper, an alignment-free and cancelable iris key binding scheme without ECC is proposed. The proposed system protects the binary biometric data, i.e., IrisCodes, from security and privacy attacks through a strong and size varying non-invertible cancelable transform. The proposed scheme provides flexibility in system storage and authentication speed via controllable hashed code length. We also proposed a fast key regeneration without either re-enrollment or constant storage of seeds. The experimental results and security analysis show the validity of the proposed scheme.
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HONG, JIN-HYUK, EUN-KYUNG YUN, and SUNG-BAE CHO. "A REVIEW OF PERFORMANCE EVALUATION FOR BIOMETRICS SYSTEMS." International Journal of Image and Graphics 05, no. 03 (July 2005): 501–36. http://dx.doi.org/10.1142/s0219467805001872.
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Biometrics is a technology designed to automatically recognize a person together with his/her natural and distinct characteristics. Recently it is in the limelight as an effective authentication method of information. With the great interests in biometrics, the need for reliable evaluation of these technologies increases and the research on objective and quantitative performance estimation methodology is actively investigated. In this paper, we give a comprehensive overview of biometric technology and performance evaluation with more than 100 publications, specially focused on fingerprints. After the thorough review, we propose a promising evaluation method based on affecting factors.
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Shawkat1, Shihab A., and Raya N. Ismail2. "Biometric Technologies in Recognition Systems: A Survey." Tikrit Journal of Pure Science 24, no. 6 (November 3, 2019): 132. http://dx.doi.org/10.25130/j.v24i6.899.
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The ability to recognize people uniquely and to associate personal attributes such as name and nationality with them has been very important to the fabric of human society. Nowadays, modern societies have an explosion in population growth and increased mobility which necessitated building advanced identity management systems for recording and maintaining people’s identities. In the last decades, biometrics has played an important role in recognizing people instead of traditional ways such as passwords and keys which can be forgotten or be stolen. Biometric systems employ physiological and/or behavioral characteristics of people to verify their identities. There are different biometric modalities that can be used to recognize people such as fingerprints, face, hand geometry, voice, iris, signature, etc. In this paper, a comprehensive overview have been provided on the major issues of biometric systems including general biometric system architecture, major biometric traits, biometric systems performance, and some relevant works.
http://dx.doi.org/10.25130/tjps.24.2019.120
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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 (September 27, 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 based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.
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Singh, Yogendra Narain, Sanjay Kumar Singh, and Amit Kumar Ray. "Bioelectrical Signals as Emerging Biometrics: Issues and Challenges." ISRN Signal Processing 2012 (July 26, 2012): 1–13. http://dx.doi.org/10.5402/2012/712032.
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This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.
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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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Grother, Patrick, and Elham Tabassi. "Performance of Biometric Quality Measures." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 4 (April 2007): 531–43. http://dx.doi.org/10.1109/tpami.2007.1019.
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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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Seo, Minhye, Jong Hwan Park, Youngsam Kim, Sangrae Cho, Dong Hoon Lee, and Jung Yeon Hwang. "Construction of a New Biometric-Based Key Derivation Function and Its Application." Security and Communication Networks 2018 (December 2, 2018): 1–14. http://dx.doi.org/10.1155/2018/6107912.
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Biometric data is user-identifiable and therefore methods to use biometrics for authentication have been widely researched. Biometric cryptosystems allow for a user to derive a cryptographic key from noisy biometric data and perform a cryptographic task for authentication or encryption. The fuzzy extractor is known as a prominent biometric cryptosystem. However, the fuzzy extractor has a drawback in that a user is required to store user-specific helper data or receive it online from the server with additional trusted channel, to derive a correct key. In this paper, we present a new biometric-based key derivation function (BB-KDF) to address the issues. In our BB-KDF, users are able to derive cryptographic keys solely from their own biometric data: users do not need any other user-specific helper information. We introduce a security model for the BB-KDF. We then construct the BB-KDF and prove its security in our security model. We then propose an authentication protocol based on the BB-KDF. Finally, we give experimental results to analyze the performance of the BB-KDF. We show that our proposed BB-KDF is computationally efficient and can be deployed on many different kinds of devices.
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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.
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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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Yaman, Mehmet, Abdulhamit Subasi, and Frank Rattay. "Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition." Symmetry 10, no. 11 (November 19, 2018): 651. http://dx.doi.org/10.3390/sym10110651.
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Biometry based authentication and recognition have attracted greater attention due to numerous applications for security-conscious societies, since biometrics brings accurate and consistent identification. Face biometry possesses the merits of low intrusiveness and high precision. Despite the presence of several biometric methods, like iris scan, fingerprints, and hand geometry, the most effective and broadly utilized method is face recognition, because it is reasonable, natural, and non-intrusive. Face recognition is a part of the pattern recognition that is applied for identifying or authenticating a person that is extracted from a digital image or a video automatically. Moreover, current innovations in big data analysis, cloud computing, social networks, and machine learning have allowed for a straightforward understanding of how different challenging issues in face recognition might be solved. Effective face recognition in the enormous data concept is a crucial and challenging task. This study develops an intelligent face recognition framework that recognizes faces through efficient ensemble learning techniques, which are Random Subspace and Voting, in order to improve the performance of biometric systems. Furthermore, several methods including skin color detection, histogram feature extraction, and ensemble learner-based face recognition are presented. The proposed framework, which has a symmetric structure, is found to have high potential for biometrics. Hence, the proposed framework utilizing histogram feature extraction with Random Subspace and Voting ensemble learners have presented their superiority over two different databases as compared with state-of-art face recognition. This proposed method has reached an accuracy of 99.25% with random forest, combined with both ensemble learners on the FERET face database.
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El Beqqal, Mohamed, Mostafa Azizi, and Jean Louis Lanet. "Multimodal access control system combining RFID, fingerprint and facial recognition." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 405. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp405-413.
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<span>Monomodal biometry does not constitute an effective measure to meet the desired performance requirements for large-scale applications, due to limita-tions such as noisy data, restricted degree of freedom and unacceptable error rates. Some of these problems can be solved through multimodal biometric systems that involve using a combination of two or more biometric modali-ties in a single identification system. Identification based on multiple biomet-rics represents an emerging trend. The reason for combining different modal-ities is to improve the recognition rate. In practice, multi-biometric aims to reduce the False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) which are two standard metrics widely used in the accuracy of biometric sys-tems. In this paper, we will examine the different possible scenario in multi-modal biometric systems using RFID, fingerprint and facial recognition, that can be adopted to merge information and improve the overall accuracy of the system.</span>
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S. Jahromi, Mohammad N., Pau Buch-Cardona, Egils Avots, Kamal Nasrollahi, Sergio Escalera, Thomas B. Moeslund, and Gholamreza Anbarjafari. "Privacy-Constrained Biometric System for Non-Cooperative Users." Entropy 21, no. 11 (October 24, 2019): 1033. http://dx.doi.org/10.3390/e21111033.
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With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.
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Connolly, Christine. "Performance testing of commercial biometric systems." Sensor Review 26, no. 1 (January 2006): 33–37. http://dx.doi.org/10.1108/02602280610640652.
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Lazarick, Richard T. "Multi-purpose biometric performance grading scheme." International Journal of Biometrics 5, no. 1 (2013): 99. http://dx.doi.org/10.1504/ijbm.2013.050735.
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Hollingsworth, Karen, Kevin W. Bowyer, and Patrick J. Flynn. "Pupil dilation degrades iris biometric performance." Computer Vision and Image Understanding 113, no. 1 (January 2009): 150–57. http://dx.doi.org/10.1016/j.cviu.2008.08.001.
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Yang, Wencheng, Jiankun Hu, Song Wang, and Qianhong Wu. "Biometrics Based Privacy-Preserving Authentication and Mobile Template Protection." Wireless Communications and Mobile Computing 2018 (June 12, 2018): 1–17. http://dx.doi.org/10.1155/2018/7107295.
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Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template.
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Kukula, E. P., M. J. Sutton, and S. J. Elliott. "The Human–Biometric-Sensor Interaction Evaluation Method: Biometric Performance and Usability Measurements." IEEE Transactions on Instrumentation and Measurement 59, no. 4 (April 2010): 784–91. http://dx.doi.org/10.1109/tim.2009.2037878.
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KHUWAJA, GULZAR A., and A. N. ABU-REZQ. "DATA ACQUISITION AND RECOGNITION OF FINGERPRINTS WITH LVQ." International Journal of Computational Intelligence and Applications 03, no. 01 (March 2003): 65–88. http://dx.doi.org/10.1142/s1469026803000811.
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A typical biometric system has three distinct phases. These are biometric data acquisition, feature extraction, and decision-making. The first step, the acquisition phase, is extremely important. If high quality images are not obtained, the next phase cannot operate reliably. Fingerprint recognition remains as one of the most prominent biometric identification methods. In this paper, we develop a prototype optical-based fingerprints data acquisition system using a CCD digital still camera to capture a complete impression of finger area required for accurately identifying an individual and present an image-based approach for online fingerprint recognition with the objective to increase the overall matching performance. The fingerprint images are matched based on features extracted with an adaptive learning vector quantization (LVQ) neural network to yield peak recognition of 98.6% for a random set of 300 test prints (100 fingers × 3 images). This system can be adopted as a multi-modal biometrics where two or more fingers are matched.
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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 (July 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 process and increases the numerical accuracy substantially. The proposed iris localization scheme is robust against poor localization and weak iris/sclera boundaries. Second, a modified local binary pattern (MLBP), which combines both the sign and magnitude features for the improvement of recognition performance, is applied. Third, to select the optimal subset of features from the fused feature vector, a feature subset selection scheme based on random forest (RF) is proposed. To evaluate the performance of the proposed scheme, the facial images of Yale Extended B Face database are fused with the iris images of CASIA V4 interval dataset to construct an iris–face multimodal biometric dataset. The experimental results indicate that the proposed multimodal biometrics system is more reliable and robust than the unimodal biometric scheme.
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Bhatnagar, Jay, and Ajay Kumar. "On estimating performance indices for biometric identification." Pattern Recognition 42, no. 9 (September 2009): 1803–15. http://dx.doi.org/10.1016/j.patcog.2008.10.004.
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Tiwari, Shrikant, and Sanjay Kumar Singh. "Newborn Verification Using Headprint." Journal of Information Technology Research 5, no. 2 (April 2012): 15–30. http://dx.doi.org/10.4018/jitr.2012040102.
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Missing, swapping, mixing, and illegal adoption of newborns is a worldwide challenge and research done to resolve this issue is minimal and least reported in the literature. Most of the biometric systems developed are for adults and very few of them address the issue of newborn identification. The headprint of a newborn is an important source of data for passive recognition, as they are non cooperative users of biometrics. The objective of this paper is to demonstrate the concept of using headprint recognition for verification of newborns. The main contributions of the research are: (1) The preparation of headprint biometric database of newborn and (2) Performance and analysis of the appearance and texture based algorithm for verification of 200 newborn.
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Ng’ang’a, Allan, and Paula M. W. Musuva. "Enhancing Accuracy in a Touch Operation Biometric System: A Case on the Android Pattern Lock Scheme." Mobile Information Systems 2020 (June 10, 2020): 1–12. http://dx.doi.org/10.1155/2020/4165457.
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The main objective of this research study is to enhance the functionality of an Android pattern lock application by determining whether the time elements of a touch operation, in particular time on dot (TOD) and time between dot (TBD), can be accurately used as a biometric identifier. The three hypotheses that were tested through this study were the following–H1: there is a correlation between the number of touch stroke features used and the accuracy of the touch operation biometric system; H2: there is a correlation between pattern complexity and accuracy of the touch operation biometric system; H3: there is a correlation between user training and accuracy of the touch operation biometric system. Convenience sampling and a within-subjects design involving repeated measures were incorporated when testing an overall sample size of 12 subjects drawn from a university population who gave a total of 2,096 feature extracted data. Analysis was done using the Dynamic Time Warping (DTW) Algorithm. Through this study, it was shown that the extraction of one-touch stroke biometric feature coupled with user training was able to yield high average accuracy levels of up to 82%. This helps build a case for the introduction of biometrics into smart devices with average processing capabilities as they would be able to handle a biometric system without it compromising on the overall system performance. For future work, it is recommended that more work be done by applying other classification algorithms to the existing data set and comparing their results with those obtained with DTW.
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Rajagopal, Gayathri, and Ramamoorthy Palaniswamy. "Performance Evaluation of Multimodal Multifeature Authentication System UsingKNN Classification." Scientific World Journal 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/762341.
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This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.
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Alhamdani, Israa Mohammed, and Yahya Ismail Ibrahim. "Swarm intelligent hyperdization biometric." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 385. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp385-395.
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At the last decade the importance of biometrics has been clearly configured due to its important in the daily life that starts from civil applications with security and recently terrorizing. A Footprint recognition is one of the effective personal identifications based on biometric measures. The aim of this research is to design a proper and reliable left human footprint biometrics system addressed (LFBS). In addition, to create a human footprint database which it is very helpful for numerous use such as during authentication. The existing footprint databases were very rare and limited. This paper presents a sturdy combined technique which merges between Image Processing with Artificial Intelligent technique via Bird Swarm Optimization Algorithm (BSA) to recognize the human footprint. The use of (BSA) enhance the performance and the quality of the results in the biometric system through feature selection. The selected features was treated as the optimal feature set in standings of feature set size. The visual database was constructed by capturing life RGB footprint images from nine person with ten images per person. The visual dataset images was pre-processed by successive operations. Chain Code is used with footprint binary image, then statistical features which represent the footprint features. These features were extracted from each image and stored in Excel file to be entered into the Bird Swarm Algorithm. The experimental results show that the proposed algorithm estimates, excellent results with a smaller feature set in comparison with other algorithms. Experimental outcomes show that our algorithm achieves well-organized and accurate result about 100% accuracy in relation with other papers on the same field.
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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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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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Sirotin, Yevgeniy B., Jacob A. Hasselgren, and Arun Vemury. "Usability of Biometric Iris-Capture Methods in Self-Service Applications." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 2019–23. http://dx.doi.org/10.1177/1541931213601459.
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Biometric technologies are gaining wide acceptance as a means of securely establishing individual identity. Common biometric modalities include recognition of fingerprints, faces, and irises. However, increased adoption of biometric technologies poses a significant challenge to users within a public setting. Users generally have little prior experience using biometric devices and prior evaluations have not systematically evaluated device usability with this naïve population. We tested an array of biometric modalities and methods both staffed and unstaffed use-case scenarios with a representative naïve subject population. Here we report on the usability of three iris collection methods: user-controlled, operator-controlled, and mechanically-controlled. Biometric performance was strongly determined by subjects’ ability to correctly position and gaze at the iris device. Performance was poorest for the user-controlled method. Usability issues are the main differentiators of iris collection methods for a naïve user population.
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Ibrahim, Yahya Ismail, and Israa Mohammed Alhamdani. "A hyprid technique for human footprint recognition." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 4060. http://dx.doi.org/10.11591/ijece.v9i5.pp4060-4068.
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Biometrics has concerned a great care recently due to its important in the life that starts from civil applications to security and recently terrorism. A Footprint recognition is one of the personal identifications based on biometric measurements. The aim of this research is to design a proper and reliable biometric system for human footprint recognition named (FRBS) that stands for Footprint Recognition Biometric System. In addition, to construct a human footprint database which it is very helpful for various use in scientific application e.g. for authentication. There exist many biometrics databases for other identity but very rare for footprint. As well as the existing one are very limited. This paper presents a robust hyprid techniques which merges between Image Processing with Artificial Intelligent technique via Ant Colony Optimization (ACO) to recognize human footprint. (ACO) plays the essential role that rise the performance and the quality of the results in the biometric system via feature selection. The set of the selected features was treated as exploratory information, and selects the optimum feature set in standings of feature set size. Life RGB footprint images from nine persons with ten images per person constructed from life visual dataset. At first, the visual dataset was pre-processed operations. Each resultant image detects footprint that is cropped to portions represented by three blocks. The first block is for fingers, the second block refers to the center of the foot and the last one determines the heel. Then features were extracted from each image and stored in Excel file to be entered to Ant Colony Optimization Algorithm. The experimental outcomes of the system show that the proposed algorithm evaluates optimal results with smaller feature set comparing with other algorithms. Experimental outcomes show that our algorithm obtains an efficient and accurate result about 100% accuracy in comparison with other researches on the same field.
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Włodarczyk, Michał, Paweł Krotewicz, Damian Kacperski, Wojciech Sankowski, and Kamil Grabowski. "Inter-Device Periocular Recognition Under Near-Infrared Light." Image Processing & Communications 21, no. 4 (December 1, 2016): 33–44. http://dx.doi.org/10.1515/ipc-2016-0021.
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Abstract Periocular biometrics is a relatively new field of research, and only several publications on this topic can be found in the literature. It can become a promising feature that can be used independently or as a complement to other biometrics. In this work, the recognition rates of periocular biometrics on a single acquisition device and inter-device database is verified and the impact of different image sources on the performance of recognition algorithms is investigated. For this purpose a NearInfrared Light database was collected. The database contains images taken by two acquisition devices. In order to test the periocular biometric trait, three feature extraction methods are chosen: Histograms of Oriented Gradients, Local Binary Patterns and Scale Invariant Feature Transform. The fusion of these methods is also proposed and it is tested on inter-device database. The feasibility of applying periocular recognition as an individual decision module for a biometric system is assessed. Experimental results yield Equal Error Rate of 17.65 for right eye using inter-device database of 640 gallery periocular images for each eye side taken from 32 different individuals (20 images per individual for each eye side). These results are obtained by the optimal weighted sum fusion of the three feature extraction methods.
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Badmus, E. O., O. P. Odekunle, and D. O. Oyewobi. "Smart Fingerprint Biometric and RFID Time-Based Attendance Management System." European Journal of Electrical Engineering and Computer Science 5, no. 4 (July 27, 2021): 34–39. http://dx.doi.org/10.24018/ejece.2021.5.4.339.
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Attendance documentation is a crucial metric for determining a student's dedication, qualification, and status. Several types of systems have been developed for automated attendance. The majority of these implementations were built on a single factor template, representing a line of security error. In recent years, rapid development has taken place in several applications such as authorization, data access, and access control by implementing biometrics and radio frequency identification technologies. This research work focuses on designing a smart protocol for RFID authentication in combination with fingerprint biometric information, which guarantees confidentiality, identity verification, and data protection. Reaction time and event performance evaluation were the tests performed on the system. Ten students with unique fingerprints and RFID cards are assessed for the test. The test results show an average duration of 20.61 respective seconds for each student, and zero percent of false refusal rate was recorded in the implementation. Finally, the result shows the system is reliable and efficient. Each result revealed high-speed performance due to software and hardware coordination. Biometric systems provided data integrity and security, RFID provided limited complexity, and the proposed method's technical programming pattern provided an average run time.
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Chai, Tong-Yuen, Bok-Min Goi, and Wun-She Yap. "Towards Better Performance for Protected Iris Biometric System with Confidence Matrix." Symmetry 13, no. 5 (May 20, 2021): 910. http://dx.doi.org/10.3390/sym13050910.
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Biometric template protection (BTP) schemes are implemented to increase public confidence in biometric systems regarding data privacy and security in recent years. The introduction of BTP has naturally incurred loss of information for security, which leads to performance degradation at the matching stage. Although efforts are shown in the extended work of some iris BTP schemes to improve their recognition performance, there is still a lack of a generalized solution for this problem. In this paper, a trainable approach that requires no further modification on the protected iris biometric templates has been proposed. This approach consists of two strategies to generate a confidence matrix to reduce the performance degradation of iris BTP schemes. The proposed binary confidence matrix showed better performance in noisy iris data, whereas the probability confidence matrix showed better performance in iris databases with better image quality. In addition, our proposed scheme has also taken into consideration the potential effects in recognition performance, which are caused by the database-associated noise masks and the variation in biometric data types produced by different iris BTP schemes. The proposed scheme has reported remarkable improvement in our experiments with various publicly available iris research databases being tested.
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Sarkar, Arpita, and Binod K. Singh. "A review on performance,security and various biometric template protection schemes for biometric authentication systems." Multimedia Tools and Applications 79, no. 37-38 (July 29, 2020): 27721–76. http://dx.doi.org/10.1007/s11042-020-09197-7.
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Narang, Ashima, Deepali Gupta, and Amandeep Kaur. "Biometrics-Based Un-Locker to Enhance Cloud Security Systems." International Journal of Cloud Applications and Computing 10, no. 4 (October 2020): 1–12. http://dx.doi.org/10.4018/ijcac.2020100101.
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With the increasing demand for cloud computing, its challenges are also increasing, and security is one of the preeminent concerns. A number of approaches were introduced earlier to secure cloud storage systems, but many of them failed to provide security effectively and even emanate the concern of cost. Efficiency and cost are the essential features for any cloud system and demand for a security mechanism. To accomplish this objective, a novel feature is introduced with a concept of biometric authentication and named as ‘biometric-based un-locker'. Biometrics entities are unique in nature, so it will be the best and most cost-effective solution for the cloud. The performance of this proposed system is analyzed by generating a cloud environment and implementing it using the .NET framework on five different datasets. The results are calculated with both attackers and normal users, and performance is analyzed on the basis of time, size of data after encryption, and authentication rate. Results show that the proposed system effectively provides security and is also very cost-efficient.