An efficient P-KCCA algorithm for 2D-3D face recognition using SVM

Abstract

In this paper, a novel face recognition system for face recognition and identification based on a combination of Principal Component Analysis and Kernel Canonical Correlation Analysis (P-KCCA) using Support Vector Machine (SVM) is proposed. First, the P-KCCA method is utilized to detect and extract the important features from the input images. This method makes it possible to match the 2D face image with enrolled 3D face data. The resulting features are then classified using the SVM method. The proposed methods were tested on TEXAS database with 200 subjects. The experimental results in the TEXAS face database produce interesting results from the point of view of recognition success, rate, and robustness of the face recognition algorithm. We compare the performance of our proposed face recognition method to other commonly-used methods. The experimental results show that the combination of P-KCCA method using SVM achieves a higher performance compared to the alone PCA, CCA and KCCA algorithms.