Informatics Publishing Limited

D. R. Ambika ¹, K. R. Radhika ¹, D. Sehsachalam ¹

Affiliations
1 BMS College of Engineering, Bangalore - 19, IN

Abstract

Biometrics is science of measuring and statistically analyzing biological data. Biometric system establishes identity of a person based on unique physical or behavioural characteristic possessed by an individual. Behavioural biometrics measures characteristics which are acquired naturally over time. Physical biometrics measures inherent physical characteristics on a n individual. Over the last few decades enormous attention is drawn towards ocular biometrics. Cues provided by ocular region have led to exploration of newer traits. Feasibility of periocular region as a useful biometric trait has been explored recently. With the promising results of preliminary examination, research towards periocular region is currently gaining lot of prominence. Researchers have analyzed various techniques of feature extraction and classification in the periocular region. The current paper investigates the effect of using Lower Central Periocular Region (LCPR) for identification. The results obtained are comparable with those acquired for the entire periocular region with an advantage of reduced periocular area.

Keywords

Periocular Region, Lower Central Periocular Region and Ocular Biometrics.

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S. V. Sheela ¹, K. R. Radhika ¹

Affiliations
1 B. M. S. College of Engineering, Bangalore, IN

Abstract

The physiological and behavioral trait is employed to develop biometric authentication systems. The proposed work deals with the authentication of iris and signature based on minimum variance criteria. The iris patterns are preprocessed based on area of the connected components. The segmented image used for authentication consists of the region with large variations in the gray level values. The image region is split into quadtree components. The components with minimum variance are determined from the training samples. Hu moments are applied on the components. The summation of moment values corresponding to minimum variance components are provided as input vector to k-means and fuzzy kmeans classifiers. The best performance was obtained for MMU database consisting of 45 subjects. The number of subjects with zero False Rejection Rate [FRR] was 44 and number of subjects with zero False Acceptance Rate [FAR] was 45. This paper addresses the computational load reduction in off-line signature verification based on minimal features using k-means, fuzzy k-means, k-nn, fuzzy k-nn and novel average-max approaches. FRR of 8.13% and FAR of 10% was achieved using k-nn classifier. The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. The system aims to provide simple, fast and robust system using less number of features when compared to state of art works.

Keywords

Off-Line Signature, Iris, Minimum Variance Quadtree Components, Hu Moments.

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