Level feature fusion of multispectral palmprint recognition using the ridgelet transform and OAO multi-class classifier

The performance of a biometric system is based primarily on the quality of physical or behavioral biometric used for a robust and an accurate authentication/identification of an individual. To improve the performance and the robustness of the system, multispectral palmprint images were employed to acquire more discriminative information. In this paper, we introduce a novel multispectral recognition method. In this context, we propose the fusion of palmprint and palm vein features to increase the accuracy of the biometric person recognition. The proposed approach is based on statistical study and energy distribution analysis of Finite Ridgelet transform coefficients, involving so low computation complexity. For multispectral palmprint images recognition, we tested the performance of three classifiers: K nearest neighbor (KNN), Support Vector Machine (SVM) and `One-Against-One´ multi-class SVM (OAO-SVM) with RBF kernel using 6-folders cross-validation to assess the generalization capability of the proposed biometric system. The validation of our results is performed on multispectral palmprint images of CASIA database.