Research On Illumination Processing Algorithms For Face Recognition

Face recognition, a non-contact and friendly biometric identification technology, has broad application prospects in the military, public security and economic security. Face recognition has received significant attention in pattern recognition, image processing, computer vision, cognitive science and neural networks. Related research in recent years has made great progress, a number of excellent face recognition algorithms have emerged, and a number of face recognition systems have achieved good performance. However, many issues still remain to be addressed and illumination changes remain one of the major challenges for current face recognition systems. The report of FRVT 2006 shows that varying illumination will seriously affect the performance of face recognition. To deal with the illumination variation problem, this dissertation focuses on illumination normalization and extracting illumination invariants, and introduces vision model, homomorphic filtering, and multiscale analysis theory into research on illumination problem, trying to eliminate the effects of illumination changes on face recognition. The main contributions are as follows.(1) A homomorphic filtering based illumination normalization method is presented.According to Lambert illumination model, the homomorphic filtering method is introduced to dealing with illumination variation problem for face recognition, in which the Difference of Gaussian filter with band-pass characteristics replaces the high-pass filter used commonly. Moreover, a histogram equalization process is added. This illumination normalization method is simple and efficient, and can effectively eliminate the adverse effects of illumination variation. Experimental results verify the effectiveness of the method.(2)An improved wavelet-based illumination normalization method is proposed.Wavelet transform is one of the multiscale analysis tools, and is able to extract key facial features from face images severely affected by illumination conditions. There are deficiencies in existing wavelet-based illumination processing algorithms when dealing with high frequency component. To address this issue, an improved algorithm is presented. First, a logarithmic transformation is added before the wavelet transform, and which will not only take the multiplicative Lambert illumination model into additive model in log-domain, but also improve image contrast. Second, a wavelet threshold de-noising is used in high frequency processing part in order to remove mixed noise. Experimental results demonstrate the significant performance improvement of the proposed method.(3) A homomorphic filtering and LoG operator based illumination invariants extracting method is presented.Studies have shown that the image edge is not sensitive to illumination variation. In addition, existing illumination processing algorithms based on edge features are always applied edge detection on face images directly. The extracted edge using these methods will have aliasing artifacts disadvantageous to face recognition when the light is changed greatly, especially when images contains shadows. To deal with this issue, the illumination normalization preprocessing is introduced to the edge map algorithm. Therefore, the edge detection is used after illumination normalization. This obtains good results.(4) An illumination invariant method based on Gabor phase feature is proposed.Gabor wavelet transform is insensitive to external environment such as illumination, facial expressions, gestures, and occlusion, etc., and can extract robust facial feature representation. Most existing methods based on Gabor features always use the Gabor magnitude features and discard the phase features. However, studies have shown that the phase information contains a number of effective image features, and is insensitive to illumination variation. Inspired by this, the Gabor phase features are extracted as illumination invariant in this dissertation. The 2-D symmetric real Gabor wavelet is chosen in our method in order to not only avoiding the complexity of complex calculations, but also fitting the symmetry of the face image itself. A superior light treatment effect is achieved, and the computational complexity is greatly reduced.(5)An improved illumination invariant extracting algorithm based on nonsubsampled Contourlet transform is presented.Contourlet transform is a new multiscale geometric analysis method with multi-directional selectivity and anisotropy, and has a unique advantage in representing the geometrical features such as edges and texture of images. This dissertation improves the NSCT and LNSCT algorithm in the way of processing the low frequency component. The low frequency component is applied histogram equalization, which not only retains the useful information for identification, but also effectively eliminates the impact of illumination variation. This results in good illumination eliminating effects. Experimental results verify the effectiveness of improved method.Compared with other methods, the proposed methods have the following advantages.â‘ There is no need to any prior information on 3D face shape and light sources assumption;â‘¡there is no need to many training samples, thus our methods can be directly applied to single training image per person condition; andâ‘¢they are simple and computationally fast, and can be applied to the real face recognition system.