Pose Invariant Face Recognition Using Probability Distribution Functions in Different Color Channels

Abstract

In this letter a new and high performance pose invariant face recognition system based on the probability distribution functions (PDF) of pixels in different color channels is proposed. The PDFs of the equalized and segmented face images are used as statistical feature vectors for the recognition of faces by minimizing the Kullback-Leibler distance (KLD) between the PDF of a given face and the PDFs of faces in the database. Feature vector fusion (FVF) and majority voting (MV) methods have been employed to combine feature vectors obtained from different color channels in HSI and YCbCr color spaces to improve the recognition performance. The proposed system has been tested on the FERET and the Head Pose face databases. The recognition rates obtained using FVF approach for FERET database is 98.00% compared with 94.60% and 68.80% for MV and principle component analysis (PCA)-based face recognition techniques, respectively.