Feature extraction and discriminating feature selection for 3D face recognition

This paper presents a 3D face recognition method. In this method, 3D discrete cosine transform (DCT) is used to extract features. Before the feature extraction, faces are aligned with respect to nose tip and then registered two times: according to average nose and average face. Then the coefficients of 3D transformation are calculated. The most discriminating 3D transform coefficients are selected as the feature vector where the ratio of between-class variance and within-class variance is used for discriminant coefficient selection. The results show that the most energetic features, low frequency components, are not the most discriminating features. The method was also modified based on 3D discrete Fourier transform (DFT) for feature selection as regarding real and complex DFT coefficients as independent features. Discriminating features were matched by using the nearest neighbor classifier. Recognition experiments were realized on 3D RMA face database. The proposed method yields a recognition rate above 99% for 3D DCT based features.