Multi-resolution fusion of DTCWT and DCT for shift invariant face recognition

A novel Multi-Resolution Fusion (MRF) of Dual-Tree Complex Wavelet Transform (DTCWT) and Discrete Cosine Transform (DCT) is introduced in this paper. Shift invariant multi-scale feature set is obtained using 2D DTCWT. Subsequently, discriminant DCT coefficients are extracted to map the high dimensional features into low dimensional subspace. The resulting feature vector contains non-redundant discriminative information and is small in size. Therefore, the proposed face recognition technique exhibits computational efficiency, low storage requirement along with high recognition rate under varying shift conditions. It also provides robustness to expression and illumination change. The performance evaluation is accomplished on four standard face databases. Experimental results show significant performance improvement over existing well-established face recognition methods under varying conditions.