Local Contrast Enhancement for Human Face Recognition in Poor Lighting Conditions

• Main Authors: Ming-Chai Hsu, 徐民儕
• Other Authors: Wen-Chung Kao
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/cas243

碩士 === 國立臺灣師範大學 === 應用電子科技學系 === 96 === In recent years, many face recognition algorithms have been developed for surveillance systems and promising results have been reported in specific environments. The human face recognition highly relies on extracted stable features from input images. In practical application environments, however, the direction of the illuminant is uncontrollable and it will result in unstable feature extraction. For remedying the problems caused by non-uniform light sources, illumination compensation is necessary. In this thesis, we propose a local contrast enhancement approach to reduce the effect of non-uniform light sources, and integrate it with a face recognition system. Through the process of local contrast enhancement, the facture extraction based on digital cosine transformation (DCT) becomes more reliable. The adopted classification kernel is support vector machines (SVM) which has been shown to be a robust classifier. The well-known human face database Yale_B is used for verifying system performance, and the recognition rate can achieve to 99.13%. As far as we known, the recognition rate is better than all of the published literatures.