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This paper introduces an efficient method for face localization and recognition in color images. The proposed method uses the location of eyes for computation and extraction of a face's bounding ellipse. In this way, parameters of a face's ellipse (center, orientation, major and minor axis), is computed by the location of eyes in a face image. In the next step, we apply Pseudo Zernike Moments (PZM), Zernike Moments (ZM) and Principal Component Analysis (PCA) for feature extraction. For classification of these feature vectors a new structure of RBF neural networks with a novel distance function is introduced and a new method for determination of RBF unit parameters is proposed. Finally, we compare the efficiency of the proposed system for three types of feature vectors (PZM, ZM and PCA). Results emphasize the high accuracy and efficiency of the PZM features proportion to other features (ZM and PCA) for use in the proposed recognition system.