Unlocking Electronic Lock using Biometrics

Being an engineering student, it often happens students have to carry a lot of things to college: mini drafter, project models, football, laptop et cetera. There is always a chance of breaking, losing these things while travelling to and from college. Students forget their assignments and submissions on their due dates. So out of curiosity we began wondering about possibility of having lockers in our college. How better it would be for students! All the unwanted stuff for the lecture can be stuffed inside the lockers till the time required. So this project aims at implementing a way to control electronic lock in the locker to unlock when the user has his identity identified by using biometrics. Biometrics is a growing research and development field. By using biometrics for user identity verification in our project we are encouraging ourselves to explore the field of biometrics which has tremendous potential in the near-future

Deployment of Keystroke Analysis on a Smartphone

The current security on mobile devices is often limited to the Personal Identification Number (PIN), a secretknowledge based technique that has historically demonstrated to provide ineffective protection from misuse. Unfortunately, with the increasing capabilities of mobile devices, such as online banking and shopping, the need for more effective protection is imperative. This study proposes the use of two-factor authentication as an enhanced technique for authentication on a Smartphone. Through utilising secret-knowledge and keystroke analysis, it is proposed a stronger more robust mechanism will exist. Whilst keystroke analysis using mobile devices have been proven effective in experimental studies, these studies have only utilised the mobile device for capturing samples rather than the more computationally challenging task of performing the actual authentication. Given the limited processing capabilities of mobile devices, this study focuses upon deploying keystroke analysis to a mobile device utilising numerous pattern classifiers. Given the trade-off with computation versus performance, the results demonstrate that the statistical classifiers are the most effective

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

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

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

Biometric cryptosystem using online signatures

Biometric cryptosystems combine cryptography and biometrics to benefit from the strengths of both fields. In such systems, while cryptography provides high and adjustable security levels, biometrics brings in non-repudiation and eliminates the need to remember passwords or to carry tokens etc. In this work we present a biometric cryptosystems which uses online signatures, based on the Fuzzy Vault scheme of Jules et al. The Fuzzy Vault scheme releases a previously stored key when the biometric data presented for verification matches the previously stored template hidden in a vault. The online signature of a person is a behavioral biometric which is widely accepted as the formal way of approving documents, bank transactions, etc. As such, biometric-based key release using online signatures may have many application areas. We extract minutiae points (trajectory crossings, endings and points of high curvature) from online signatures and use those during the locking & unlocking phases of the vault. We present our preliminary results and demonstrate that high security level (128 bit encryption key length) can be achieved using online signatures