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The 2020 International Joint Conference on Biometrics (IJCB 2020) combines two major biometrics research conferences, the Biometrics Theory, Applications and Systems (BTAS) conference and the International Conference on Biometrics (ICB) The blending of these two conferences in 2020 is through special agreement between the IEEE Biometrics Council and the IAPR TC 4, and should present an exciting event for the entire worldwide biometrics research community.
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A biometric lifeless attack is one of the indispensable issues within biometric authentication. There are three major components in liveness detection systems: lifeless attack presentation, liveness detection, and lifeless attack instruments. The lifeless attack presentation is divided into artifact presentation and human-based presentation. The liveness detection method includes subject-based and scenario-based solutions, as well as other attributes such as decision elements, detection patterns, and implementations. The lifeless attack instrument is specified from aspects such as production elements, production types of artifacts, efficacy, etc. This document establishes terms and definitions in the field of biometric liveness detection and identifies characterizations of lifeless attack and liveness detection methods, with analysis on lifeless attack instruments. In addition, this document specifies the liveness detection process, implementation model, and metrics.
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The Standard for Biometric Privacy (SBP) provides private identity assertion. SBP supersedes the prior IEEE Std 2410(TM)-2019 by including a formal specification for privacy and biometrics such that a conforming SBP system does not incur GDPR, CCPA, BIPA or HIPAA privacy obligations. Homomorphic encryption ensures the biometric payload is always one-way encrypted with no need for key management and provides full privacy by ensuring plaintext biometrics are never received by the SBP server. The SBP implementation includes software running on a client device and on the SPB server. Pluggable components are used to replace legacy functionality to allow rapid integration into existing operating environments. The SBP implementation allows the systems to meet security needs by using the application programming interface, whether the underlying system is a relational database management system or a search engine. The SBP implementation functionality offers a "point-and-cut" mechanism to add the appropriate security to the production systems as well as to the systems in development. The architecture is language neutral, allowing Representational State Transfer (REST ), JavaScript Object Notation (JSON), and Transport Layer Security (TLS) to provide the communication interface. This document describes the essential methodology to SBP.
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In many cases of serious crime, images of a hand can be the only evidence available for the forensic identification of the offender. As well as placing them at the scene, such images and video evidence offer proof of the offender committing the crime. The knuckle creases of the human hand have emerged as an effective biometric trait and been used to identify the perpetrators of child abuse in forensic investigations. However, manual utilization of knuckle creases for identification is highly time consuming and can be subjective, requiring the expertise of experienced forensic anthropologists whose availability is very limited. Hence, there arises a need for an automated approach for localization and comparison of knuckle patterns. In this paper, we present a fully automatic end-to-end approach which localizes the minor, major and base knuckles in images of the hand, and effectively uses them for identification achieving state-of-the-art results. This work improves on existing approaches and allows us to strengthen cases further by objectively combining multiple knuckles and knuckle types to obtain a holistic matching result for comparing two hands. This yields a stronger and more robust multi-unit biometric and facilitates the large-scale examination of the potential of knuckle-based identification. Evaluated on two large landmark datasets, the proposed framework achieves equal error rates (EER) of 1.0-1.9%, rank-1 accuracies of 99.3-100% and decidability indices of 5.04-5.83. We make the full results available via a novel online GUI to raise awareness with the general public and forensic investigators about the identifiability of various knuckle regions. These strong results demonstrate the value of our holistic approach to hand identification from knuckle patterns and their utility in forensic investigations.
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Requirements and testing methods for a facial recognition testing system for end user devices are discussed in this standard. System and security requirements, algorithm testing, testing methods, and criteria-level definitions are defined. Key metrics for quantized performance evaluation indexes including false accept rate (FAR), false reject rate (FRR), attack presentation false acceptance (APFAR), and bona fide presentation false rejection rate (BFPFRR) are also defined. Criteria levels for testing the performance indexes are specified, including the detailed steps, environment, and minimum requirements.
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Biometric Technologies and Verification Systems is organized into nine parts composed of 30 chapters, including an extensive glossary of biometric terms and acronyms. It discusses the current state-of-the-art in biometric verification/authentication, identification and system design principles. It also provides a step-by-step discussion of how biometrics works; how biometric data in human beings can be collected and analyzed in a number of ways; how biometrics are currently being used as a method of personal identification in which people are recognized by their own unique corporal or