A schema for cryptographic keys generation using hybrid biometrics

Biometric identifiers refer to unique physical properties or behavioural attributes of individuals. Some of the well known biometric identifiers are voice, finger prints, retina or iris, facial structure etc. In our daily interaction with others directly or indirectly, we implicitly use biometrics to know, distinguish and trust people. Biometric identifiers represent the concept of "who a person is" by gathering vital characteristics that don't correspond to any other person. The human brain to some extent is able to ascertain disparities or variation in certain physical attributes and yet verify the authenticity of a person. But this is difficult to be implemented in electronic systems due to the intense requirements of artificial decision making and hard-coded logic. This paper examines the possibility of using a combination of biometric attributes to overcome common problems in having a single biometric scheme for authentication. It also investigates possible schemes and features to deal with variations in Biometric attributes. The material presented is related to ongoing research by the Computer Communications Research Group at Leeds Metropolitan University. We use this paper as a starting step and as a plan for advanced research. It offers ideas and proposition for implementing hybrid biometrics in conjunction with cryptography. This is work in progress and is in a very preliminary stage

A New Biometric Template Protection using Random Orthonormal Projection and Fuzzy Commitment

Biometric template protection is one of most essential parts in putting a biometric-based authentication system into practice. There have been many researches proposing different solutions to secure biometric templates of users. They can be categorized into two approaches: feature transformation and biometric cryptosystem. However, no one single template protection approach can satisfy all the requirements of a secure biometric-based authentication system. In this work, we will propose a novel hybrid biometric template protection which takes benefits of both approaches while preventing their limitations. The experiments demonstrate that the performance of the system can be maintained with the support of a new random orthonormal project technique, which reduces the computational complexity while preserving the accuracy. Meanwhile, the security of biometric templates is guaranteed by employing fuzzy commitment protocol.Comment: 11 pages, 6 figures, accepted for IMCOM 201

Design and implementation of a multi-modal biometric system for company access control

This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman\u2019s method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user\u2019s face only or the serial acquisition of both the user\u2019s face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent \u201cresponse behaviors\u201d according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&T Face Database and the UBIRIS database

Multi-modal Authentication Model for Occluded Faces in a Challenging Environment

Authentication systems are crucial in the digital era, providing reliable protection of personal information. Most authentication systems rely on a single modality, such as the face, fingerprints, or password sensors. In the case of an authentication system based on a single modality, there is a problem in that the performance of the authentication is degraded when the information of the corresponding modality is covered. Especially, face identification does not work well due to the mask in a COVID-19 situation. In this paper, we focus on the multi-modality approach to improve the performance of occluded face identification. Multi-modal authentication systems are crucial in building a robust authentication system because they can compensate for the lack of modality in the uni-modal authentication system. In this light, we propose DemoID, a multi-modal authentication system based on face and voice for human identification in a challenging environment. Moreover, we build a demographic module to efficiently handle the demographic information of individual faces. The experimental results showed an accuracy of 99% when using all modalities and an overall improvement of 5.41%–10.77% relative to uni-modal face models. Furthermore, our model demonstrated the highest performance compared to existing multi-modal models and also showed promising results on the real-world dataset constructed for this study.This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education under Grant NRF-2022R1A6A3A13063417, in part by the Government of the Republic of Korea (MSIT), and in part by the National Research Foundation of Korea under Grant NRF-2023K2A9A1A01098773

A Review of Voice-Base Person Identification: State-of-the-Art

Automated person identification and authentication systems are useful for national security, integrity of electoral processes, prevention of cybercrimes and many access control applications. This is a critical component of information and communication technology which is central to national development. The use of biometrics systems in identification is fast replacing traditional methods such as use of names, personal identification numbers codes, password, etc., since nature bestow individuals with distinct personal imprints and signatures. Different measures have been put in place for person identification, ranging from face, to fingerprint and so on. This paper highlights the key approaches and schemes developed in the last five decades for voice-based person identification systems. Voice-base recognition system has gained interest due to its non-intrusive technique of data acquisition and its increasing method of continually studying and adapting to the person’s changes. Information on the benefits and challenges of various biometric systems are also presented in this paper. The present and prominent voice-based recognition methods are discussed. It was observed that these systems application areas have covered intelligent monitoring, surveillance, population management, election forensics, immigration and border control

Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric

Biometric techniques are often used as an extra security factor in authenticating human users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify users, which typically involves some user burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously authenticated user and immediately terminating that user's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) authenticated state of a careless user who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human user's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, users can be identified with, on average, 91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques