Fusion of submanifold and local texture features for palmprint authentication

In this paper, a palmprint based authentication system is proposed. The proposed system makes use of locality sensitive discriminant analysis and directional local extrema patterns. A locality sensitive discriminant analysis has the capability to project the palmprints into a new space, where the palmprints belonging to same class become closer, whereas those of different classes move far apart. Locality sensitive discriminant analysis gathers the discriminating information based on the local texture of the data and available ground truth values. A directional local extrema pattern extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. A palmprint contains several lines in various directions. A directional local extrema pattern may prove beneficial to extract edge information from palmprints. The two kinds of features are fused at score level for authentication purpose. The PolyU pamprint dataset has been used for evaluation, and the results are found promising. To establish the improvements in palmprint recognition, the results of proposed system has been compared with several well established state of the art algorithms.