An optimized palm print recognition approach using Gabor filter

Identity verification of a person always considered a wide research area. In past, Identity of a person verified by traditional token-based or knowledge-based system but in recent year's bio-metrics traits like face, finger, iris, palm print etc. become a key technology for identity verification. Palm print is also the one bio-metric trait that can be used for the efficient identification. Palm print contains many unique features like principal lines, points, ridges, textures etc. that can differentiate two person. Because palm is a large area of hand, there is a common problem of palm displacement over scanner that results in increase in false rejection rate. This paper proposed a method which first generates ROI of captured palm image then median filtering is applied to remove noise and increasing edge sharpness. Histogram equalization applied after that for contrast stretching for low resolution images. Enhanced image is then divided in sixteen equal part, texture feature is extracted from each part of image separately using different orientations of Gabor filter. The generated feature vectors of all sixteen images are then normalized to a single feature vector using n bin histogram process. This increase acceptance rate in case if palm is placed over scanner in slightly different angles because working on small areas of palm helps to extract detail features. This paper used SVM for classification of generated feature vector and Experiment performed on polyU palm print database [1].