Biometrics in Cyber Security

Computers play an important role in our daily lives and its usage has grown manifolds today. With ever increasing demand of security regulations all over the world and large number of services provided using the internet in day to day life, the assurance of security associated with such services has become a crucial issue. Biometrics is a key to the future of data/cyber security. This paper presents a biometric recognition system which can be embedded in any system involving access control, e-commerce, online banking, computer login etc. to enhance the security. Fingerprint is an old and mature technology which has been used in this work as biometric trait. In this paper a fingerprint recognition system based on no minutiae features: Fuzzy features and Invariant moment features has been developed. Fingerprint images from FVC2002 are used for experimentation. The images are enhanced for improving the quality and a region of interest (ROI) is cropped around the core point. Two sets of features are extracted from ROI and support vector machine (SVM) is used for verification. An accuracy of 95 per cent is achieved with the invariant moment features using RBF kernel in SVM

A Review Of Multilevel Multibiometric Fusion System

Biometric systems allow automatic person recognition and authenticate based on the physical or behavioral characteristic. In recent years, researchers have paid close attention to the design of efficient multi-modal biometric systems due to their ability to withstand spoof attacks. Sometimes single biometric traits fail to extract relevant information for verifying the identity of a person. Therefore, combining multiple modalities, enhanced performance reliability could be achieved. If the security level increases then multi-level fusion techniques are used. This paper discusses the many fusion levels: algorithms, level of fusion, methods used for integrating the multiple verifiers and their applications

On the use of INS to improve Feature Matching

The continuous technological improvement of mobile devices opens the frontiers of Mobile Mapping systems to very compact systems, i.e. a smartphone or a tablet. This motivates the development of efficient 3D reconstruction techniques based on the sensors typically embedded in such devices, i.e. imaging sensors, GPS and Inertial Navigation System (INS). Such methods usually exploits photogrammetry techniques (structure from motion) to provide an estimation of the geometry of the scene. Actually, 3D reconstruction techniques (e.g. structure from motion) rely on use of features properly matched in different images to compute the 3D positions of objects by means of triangulation. Hence, correct feature matching is of fundamental importance to ensure good quality 3D reconstructions. Matching methods are based on the appearance of features, that can change as a consequence of variations of camera position and orientation, and environment illumination. For this reason, several methods have been developed in recent years in order to provide feature descriptors robust (ideally invariant) to such variations, e.g. Scale-Invariant Feature Transform (SIFT), Affine SIFT, Hessian affine and Harris affine detectors, Maximally Stable Extremal Regions (MSER). This work deals with the integration of information provided by the INS in the feature matching procedure: a previously developed navigation algorithm is used to constantly estimate the device position and orientation. Then, such information is exploited to estimate the transformation of feature regions between two camera views. This allows to compare regions from different images but associated to the same feature as seen by the same point of view, hence significantly easing the comparison of feature characteristics and, consequently, improving matching. SIFT-like descriptors are used in order to ensure good matching results in presence of illumination variations and to compensate the approximations related to the estimation process