Feature extraction based on the embedded zero-tree DCT for face recognition

In this paper, a feature extraction method based on the embedded zero-tree of the discrete cosine transform (DCT) is proposed and named EZDCT. In the proposed EZDCT, the DCT is first performed and the embedded zero-tree is then utilized to exploit the relation between the DCT coefficients as in the embedded zero-tree wavelet (EZW). Therefore a subset of the most effective coefficients can be easily selected. The EZDCT and EZW feature extraction methods are compared with the well known methods such as PCA and DCT with the zigzag scan. Experiments are conducted on the standard ORL and FERET databases Experimental results show that the EZDCT achieves a significant improvement in recognition rate with high dimensionality reduction of the feature vector as compared with the other methods. To further improve the recognition performance, the EZDCT and EZW are combined.