Design of Robust Face Recognition System to Pose Variations Based on Pose Estimation : The Comparative Study on the Recognition Performance Using PCA and RBFNNs

In this study, we compare the recognition performance using PCA and RBFNNs for introducing robust face recognition system to pose variations based on pose estimation. proposed face recognition system uses Honda/UCSD database for comparing recognition performance. Honda/UCSD database consists of 20 people, with 5 poses per person for a total of 500 face images. Extracted image consists of 5 poses using Multiple-Space PCA and each pose is performed by using (2D)2PCA for performing pose classification. Linear polynomial function is used as connection weight of RBFNNs Pattern Classifier and parameter coefficient is set by using Particle Swarm Optimization for model optimization. Proposed (2D)2PCA-based face pose classification performs recognition performance with PCA, (2D)2PCA and RBFNNs.