Face recognition using VQ histogram in compressed DCT domain

In this paper, we propose a novel algorithm using vector quantization (VQ) histogram for facial image recognition in DCT domain. Firstly, feature vectors of facial image are generated by using DCT (Discrete Cosine transform) coefficients in low frequency domains. Then codevector referred count histogram, which is utilized as a very effective personal feature value, is obtained by Vector Quantization (VQ) processing. Publicly available AT&T database of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions, is used to evaluate the performance of the proposed algorithm. Experimental results show face recognition using proposed feature vector is very efficient. The highest average recognition rate of 94.8% is obtained. Furthermore, combining a region-division (RD) method to add the geometric information of the face, top1 recognition rate of 96.4% is obtained by using FB task (1195 images) in the standard FERET database.