Score-Level Fusion of Local Spatial, Scale and Directional Features Based Face Recognition Approach for Single Sample Problem

The local appearance based methods have been successfully applied to face recognition and achieved state-of-the-art performance. In this paper we propose a face representation approach which explores the information in the spatial domain along with score-level fusion of directional features in different scales. The local spatial texture features are extracted using the local MinMax binary pattern (LMinMaxBP) approach as well as with the help of the selective local texture feature approach. The dual tree complex wavelet transform (DT-CWT) provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. 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 are used to extract the multi scale facial features with good directional selectivity which improves the face recognition with small sample size in less computation. This score-level fusion approach combines information from different domains to give a good face representation for recognition. Extensive experimental results on FERET, ORL, Yale and Indian Face databases show the significant advantages of the proposed method over the existing ones.