A Hybrid Face Recognition Approach Using Local Fusion of Complex Dual-Tree Wavelet Coefficients and Ridgelet Transform

In this paper, we propose novel face recognition method based on local appearance feature extraction using hybrid mode of local ridge-let and fused dual-tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. In the dual-tree implementation, two parallel dis-crete wavelet transform (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of sub-bands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. It is insensitive to illumination variations and facial expression changes. 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The fusion of local DT-CWT coefficients of detail subbands and local Finite Ridgelet Transform (FRIT) coefficients of approxi-mate subbands of DT-CWT are used to extract the facial features which improve the face recognition with small sample size in less computation. The local features based methods have been success-fully applied to face recognition and achieved state-of-the-art per-formance. Normally most of the local appearance based methods the facial features are extracted from several local regions and concate-nated into an enhanced feature vector as a face descriptor. In this approach we divide the face into several (m×m) non-overlapped paral-lelogram blocks instead of square or rectangle blocks. The local mean and standard deviation of hybrid FRIT and fused DT-CWT coeffi-cients are used to describe the face image. Experiments, on two well-known databases, namely, Yale and ORL databases, shows the Local hybrid FRIT and fused DT-CWT approach performs well on illumi-nation, expression and perspective variant faces with single sample compared to PCA and global DT-CWT. Furthermore, in addition to the consistent and promising classification performances, our pro-posed Hybrid Local FRIT and fused DT-CWT based method has a really low computational complexity.