Abstract—Biometric user authentication techniques for security and access control have evoked an enormous interest by science, industry and society in the last two decades. But even the best single biometric system suffers from spoof attacks, intra-class variability, noise, susceptibility etc. In the realm of biometrics, the consolidation of evidence presented by multiple biometric sources is an effective way of enhancing the recognition accuracy of an authentication system. This paper proposes an authentication for a multimodal biometric system identification using two traits i.e., face and palmprint at feature extraction level. The training database consists of face and palmprint images. Principal Component Analysis method is used to extract the features from face and palmprints separately. The feature normalization and feature concatenation scheme followed by a dimensionality reduction procedure is adopted to form the feature matrix. The normalized match (distance) scores generated by respective palm and face features before fusion are used to form fused match score. The Euclidean distance and the feature distance are calculated after fusion. All three distances are used to arrive at final decision. Feedback routine implemented between the feature extraction and the matching modules of the biometric system can lead to substantial improvement in multimodal matching performance.

Index Terms—Biometrics, face, fusion, match score, palmprint.

The authors are with College of Engineering Pune, an Autonomous Institute of Government of Maharashtra. She is now with Nutan Maharashtra Institute of Engineering and Technology, Talegaon Dabhade, University of Pune, Maharashtra, India(e-mail: gsambadkar@gmail.com; ams.extc@ coep.ac.in).

Cite: Gayatri Umakant Bokade and Ashok. M. Sapkal, "Feature Level Fusion of Palm and Face for Secure Recognition," International Journal of Computer and Electrical Engineering vol. 4, no. 2, pp. 157-160, 2012.