Relative Magnitude of Gaussian Curvature Using Neural Network and Object Rotation of Two Degrees of Freedom

This paper presents color processing for face recognition systems and proposes new directions for them. We show that color information helps performance of face recognition and found that specifically YCbCr and YCg'Cr' color spaces are the most appropriate for face recognition. In this paper, the performance of the principal component analysis (PCA)-based face recognition algorithm is performed in various color spaces including RGB, HSV, YCbCr, and YCg'Cr'. The performance evaluation was conducted with the color FERET database in terms of the recognition rate. In our experimentation, robustness of the independent PCA-based algorithms with different color domains is investigated for different facial expressions and aging.