K Jaya Priya and R S Rajesh: 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 ridgelet 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 discrete wavelet transform (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of subbands 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 approximate 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 successfully applied to face recognition and achieved state-of-the-art performance. Normally most of the local appearance based methods the facial features are extracted from several local regions and concatenated into an enhanced feature vector as a face descriptor. In this approach we divide the face into several (m×m) non-overlapped parallelogram blocks instead of square or rectangle blocks. The local mean and standard deviation of hybrid FRIT and fused DT-CWT coefficients are used to describe the face image. Experiments, on two wellknown databases, namely, Yale and ORL databases, shows the Local hybrid FRIT and fused DT-CWT approach performs well on illumination, 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 proposed Hybrid Local FRIT and fused DT-CWT based method has a really low computational complexity.