Pose Invariant Color Face Recognition Based on Frequency Analysis and DLDA with Weight Score Classification

The face images mostly cover with skin color, which exists in chrominance component. That component was discharged in almost all of the previous works. In this paper, we present a method for pose invariant color face recognition, which is based on frequency analysis and DLDA with weight-score classification. The function of frequency analysis (i.e. wavelet and DCT transforms) is to extract the global facial features by selecting the dominant coefficients existing in low frequency components. In this case, the global facial features are created not only in the luminance but also in the chrominance for covering the skin color information. The weight-score is introduced in DLDA in order to reduce the overlap projected facial features. Where, the weight score, which is defined as a whole distance among the considered class and some closely classes to it, is determined by Mahalanobis distance.