Research On Face Recognition Algorithm With Variant Illaumination

Face recognition is an important research topic in image processing and computervision, which has promising applications in public security, smart surveillance, multimediaand so on. Through face recognition has achieved great progress in the decades, when itcomes to uncontrolled conditions, such as different illumination conditions, pose variation,mixture of emotions and object shelter, and the accuracy of face recognition willdramatically decline. Therefore, how to build a real-life face recognition system is a sharpchallenging topic.This paper focuses on the problem of face recognition in variant illuminationenvironment. The main purpose is to improve the recognition accuracy and robustness offace recognition system under various lighting condition. To achieve this purpose, thefocus of our work is on the image preprocessing, illumination invariant feature extraction,classification and recognition. The major work and contributions of this paper are follows:1. The research background and significance of face recognition both at home andabroad are introduced firstly in this paper. Then the methods dealing with lighting problemare summarized, that is illumination model, preprocessing and normalization, invariantfeature extraction, and the advantages and disadvantages of these methods are analyzed.2. An illumination invariant algorithm based on Nonsubsampled contourlet transform(NSCT) and NeighShrink denoise is proposed. Illumination is one of the factors that affectthe recognition efficiency. On the analysis of wavelet transform, we extract illuminationvariant feature through NSCT, which is a fully shift-invariant, multi-scale, multi-directiontransform and not only can extract more effective illumination invariant facial features butalso can get a clearer positive light image of face. A NeighShrink-based denoising model isapplied, which considers the correlations of sub-band coefficients. Thus, more usefulinformation can be restored in the high frequency sub-band coefficients, unlike some of the other approached in which too many coefficients that might contain useful informationtend to be killed. Experimental results showed that our method could extract invariantfeature more effective and obviously improve the recognition accuracy.3. Face recognition system is divided into three key points, image preprocessing,feature extraction, classification and identification. Each part helps to improve the systemperformance. A variable light face recognition algorithm involved three points based onlocal binary pattern (LBP) and linear regression model is proposed. It can obtain betterperformance, through processing the illumination at each stage. In the image preprocessingphase, an efficient preprocessing chain is adopted which contains Gamma correction,difference of Gaussian(DoG) filtering, contrast equalization and can eliminates most of theeffects of changing illumination. Then block LBP is applied to extract invariant featurewhich is robust to variant illumination. Finally, improved linear regression model is used toclassification which drops the first n principal components. The proposed approach canreduce the effect of illumination, and improve robustness and the recognition accuracy offace recognition system.