- Author:
- Stan Z. Li
Biometrics refers to automated methods of recognizing a person based on physiological or behavioral characteristics. Among the features measured are face, fingerprints, hand geometry, handwriting, iris, retina, vein, and voice. With an AZ format, the comprehensive Encyclopedia of Biometrics provides easy access to relevant information on all aspects of biometrics for those seeking entry into the broad field of biometrics. The Encyclopedia is composed of approximately 250 entries, covering all areas of Biometrics including the following: Biometric Modalities: FaceFingerprintIris and RetinaPalmprintHand GeometryVeinSignatureGaitVoice EarKeystrokeMultimodalityLip-readingOdorSkullTeethDNA Biometric Systems and Applications:System DesignSensorsLiveness and SecurityPerformance EvaluationApplicationsNon-Technical Issues (Privacy, Societal etc.)Standardization Each entry includes a definition, key words, list of synonyms, list of related entries, illustration(s), applications, and a bibliography. Most entries include useful literature references providing the reader with a portal to more detailed information on any given topic. The style of the entries in the Encyclopedia of Biometrics is expository and tutorial, making the book a practical resource for experts in the field as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest. A minimum of 250 topic entries based on principles including: (1) wide list coverage; (2) minimized overlapping among topics. Editorial Board members are proposing approximately 20 topic entries in his/her areas, with a total near 400.
Cited By
Filipi Gonçalves dos Santos C, Oliveira D, A. Passos L, Gonçalves Pires R, Felipe Silva Santos D, Pascotti Valem L, P. Moreira T, Cleison S. Santana M, Roder M, Paulo Papa J and Colombo D (2023). Gait Recognition Based on Deep Learning: A Survey, ACM Computing Surveys, 55:2, (1-34), Online publication date: 31-Mar-2023.- Mingote V, Miguel A, Ribas D, Ortega A and Lleida E (2022). aDCF Loss Function for Deep Metric Learning in End-to-End Text-Dependent Speaker Verification Systems, IEEE/ACM Transactions on Audio, Speech and Language Processing, 30, (772-784), Online publication date: 1-Jan-2022.
- Zoppi T, Gharib M, Atif M and Bondavalli A (2021). Meta-Learning to Improve Unsupervised Intrusion Detection in Cyber-Physical Systems, ACM Transactions on Cyber-Physical Systems, 5:4, (1-27), Online publication date: 31-Oct-2021.
- Malgheet J, Manshor N, Affendey L and Lopez Gutierrez R (2021). Iris Recognition Development Techniques, Complexity, 2021, Online publication date: 1-Jan-2021.
- Sagiroglu S, Ulker M and Arslan B Mobile Touchless Fingerprint Acquisition And Enhancement System 2020 IEEE Congress on Evolutionary Computation (CEC), (1-8)
- Huang W, Lin L, Huang T, Lin J and Zhang X (2019). Scale-adaptive tracking based on perceptual hash and correlation filter, Multimedia Tools and Applications, 78:12, (16011-16032), Online publication date: 1-Jun-2019.
- Mandalapu H, Ramachandra R and Busch C Empirical Evaluation of Texture-Based Print and Contact Lens Iris Presentation Attack Detection Methods Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications, (7-14)
- Dwivedi R and Dey S (2019). A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification, Applied Intelligence, 49:3, (1016-1035), Online publication date: 1-Mar-2019.
- Zheng W and Jin M Do We Need More Training Samples For Text Classification? Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference, (121-128)
- Nguyen K, Fookes C, Sridharan S, Tistarelli M and Nixon M (2018). Super-resolution for biometrics, Pattern Recognition, 78:C, (23-42), Online publication date: 1-Jun-2018.
- Banerjee A, Basu S, Basu S and Nasipuri M (2018). ARTeM, Multimedia Tools and Applications, 77:5, (5857-5884), Online publication date: 1-Mar-2018.
- Schiavone E, Ceccarelli A and Bondavalli A Continuous Biometric Verification for Non-Repudiation of Remote Services Proceedings of the 12th International Conference on Availability, Reliability and Security, (1-10)
- Angermann H, Pervez Z and Ramzan N (2017). Taxo-Semantics, Decision Support Systems, 98:C, (10-25), Online publication date: 1-Jun-2017.
- Lindeberg T (2017). Temporal Scale Selection in Time-Causal Scale Space, Journal of Mathematical Imaging and Vision, 58:1, (57-101), Online publication date: 1-May-2017.
- Sudhish P, Jain A and Cao K (2016). Adaptive fusion of biometric and biographic information for identity de-duplication, Pattern Recognition Letters, 84:C, (199-207), Online publication date: 1-Dec-2016.
- Roy H and Bhattacharjee D (2016). Heterogeneous face matching using geometric edge-texture feature (GETF) and multiple fuzzy-classifier system, Applied Soft Computing, 46:C, (967-979), Online publication date: 1-Sep-2016.
- Sapkal S and Deshmukh R Biometric Template Protection with Fuzzy Vault and Fuzzy Commitment Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, (1-6)
- Faridul H, Pouli T, Chamaret C, Stauder J, Reinhard E, Kuzovkin D and Tremeau A (2016). Colour Mapping, Computer Graphics Forum, 35:1, (59-88), Online publication date: 1-Feb-2016.
- Bringer J, Chabanne H, Favre M, Patey A, Schneider T and Zohner M GSHADE Proceedings of the 2nd ACM workshop on Information hiding and multimedia security, (187-198)
- Mehrotra H and Majhi B (2013). Local feature based retrieval approach for iris biometrics, Frontiers of Computer Science: Selected Publications from Chinese Universities, 7:5, (767-781), Online publication date: 1-Oct-2013.
- Boutellaa E, Harizi F, Bengherabi M, Ait-Aoudia S and Hadid A Face Verification Using Local Binary Patterns and Maximum A Posteriori Vector Quantization Model Proceedings, Part I, of the 9th International Symposium on Advances in Visual Computing - Volume 8033, (539-549)
- Emerich S, Lupu E and Rusu C (2013). A new set of features for a bimodal system based on on-line signature and speech, Digital Signal Processing, 23:3, (928-940), Online publication date: 1-May-2013.
- Alford A, Bryant K, Abegaz T, Dozier G, Kelly J, Shelton J, Small L, Williams J, Woodard D and Ricanek K (2012). Genetic and evolutionary methods for biometric feature reduction, International Journal of Biometrics, 4:3, (220-245), Online publication date: 1-Jul-2012.
- Zhang L, Zhang L, Zhang D and Guo Z (2012). Phase congruency induced local features for finger-knuckle-print recognition, Pattern Recognition, 45:7, (2522-2531), Online publication date: 1-Jul-2012.
- Al-Assam H and Jassim S (2012). Security evaluation of biometric keys, Computers and Security, 31:2, (151-163), Online publication date: 1-Mar-2012.
- Sun L, Huang W and Wu M TIR/VIS correlation for liveness detection in face recognition Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II, (114-121)
- Al-Assam H, Sellahewa H and Jassim S Multi-factor biometrics for authentication Proceedings of the 12th ACM workshop on Multimedia and security, (81-88)
Recommendations
Multi-factor biometrics for authentication: a false sense of security
MM&Sec '10: Proceedings of the 12th ACM workshop on Multimedia and securityMulti-factor biometric authentications have been proposed recently to strengthen security and/or privacy of biometric systems in addition to enhancing authentication accuracy. An important approach to multi-factor biometric authentication is to apply ...
Finger knuckle biometrics - A review
Display Omitted Describes the role of finger knuckle surface in biometric authentication.Enumerates various available open datasets for finger knuckle biometric trait.Presents different view point in the classification of various finger knuckle feature ...
Cancellable biometrics and annotations on BioHash
Lately, the once powerful one-factor authentication which is based solely on either password, token or biometric approach, appears to be insufficient in addressing the challenges of identity frauds. For example, the sole biometric approach suffers from ...