A Novel Binning and Indexing Approach Using Hand Geometry and Palm Print to Enhance Performance of Biometric Identification System

This paper proposes a Biometric identification system for person identification using two biometric traits hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify key points on palm region of hand. Identified key points are used to find hand geometry feature and palm print Region of interest (ROI). The discriminative palm print features are extracted by applying local binary descriptor on palm print ROI. In a biometric identification system the identity corresponding to the input image (probe) is determined by comparing probe template with the templates of all identities enrolled in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute. Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.