Biometric Palmprint identification via efficient texture features fusion

Recently, personal identification, which is based on the palmprint texture features analysis, has widely attracted the attention of several researchers and has gained a great popularity in the pattern recognition field. In this paper, we present a novel methodology based on texture information extracted from palmprint. Firstly, we propose an algorithm to robustly locate the Region Of Interest (ROI) of the hand. Secondly, we combine multiple descriptors to extract the palmprint texture information, which are Gray-Level Co-occurrence Matrix (GLCM) and the Gabor filters using feature level fusion. These descriptors have been broadly applied in various tasks, specifically in the image processing domain to analyze the image texture. Then, we apply the generalized discriminant analysis (GDA) to reduce the length of the feature vectors and their redundancies. Finally, we classify these final resulting features by developing the SVM method which supports several kernel functions to reach a best recognition rate. We have conducted extensive experiments on the “CASIA-Palmprint” and “PolyU-palmprint” datasets. The obtained results of the proposed approach provide promising results compared to other well-known state-of-the-art approaches.