Face recognition based on pixel-level and feature-level fusion of the top-level’s wavelet sub-bands

• A new method based on pixel-level fusion and feature-level fusion is proposed.
• The new method makes full use of the information at top-level’s four wavelet sub-bands.
• Two models are proposed by combining the pixel-level fusion with PCA and LDA, respectively.
• Two alternating direction methods are designed for solving the corresponding models.
• The optimal fusion coefficients and transformation matrices are obtained simultaneously.

The traditional wavelet-based approaches directly use the low frequency sub-band of wavelet transform to extract facial features. However, the high frequency sub-bands also contain some important information corresponding to the edge and contour of face, reflecting the details of face, especially the top-level’s high frequency sub-bands. In this paper, we propose a novel technique which is a joint of pixel-level and feature-level fusion at the top-level’s wavelet sub-bands for face recognition. We convert the problem of finding the best pixel-level fusion coefficients of high frequency wavelet sub-bands to two optimization problems with the help of principal component analysis and linear discriminant analysis, respectively; and propose two alternating direction methods to solve the corresponding optimization problems for finding transformation matrices of dimension reduction and optimal fusion coefficients of the high frequency wavelet sub-bands. The proposed methods make full use of four top-level’s wavelet sub-bands rather than the low frequency sub-band only. Experiments are carried out on the FERET, ORL and AR face databases, which indicate that our methods are effective and robust.