A wavelet-domain local feature selection scheme for face recognition

A multi-resolution feature extraction algorithm for face recognition based on two-dimensional discrete wavelet transform (2D-DWT) is proposed in this paper, which exploits the local spatial variations in a face image effectively. Instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal bands from the face image for feature extraction. In order to capture the local spatial variations within these bands precisely, a histogram-based local dominant feature selection criterion is proposed. The proposed dominant wavelet coefficients, in terms of frequency of occurrence, corresponding to each local region residing inside those horizontal bands not only reduces the feature dimension drastically but also provides high within-class compactness and high between-class separability. Extensive experimentation is carried out upon standard face databases and in comparison to those obtained by some of the existing methods, a very high degree of recognition accuracy is achieved by the proposed method.