An Improved Illumination Compensation Mechanism for Face Recognition System

• Main Authors: Chien-Lin Chiou, 邱建霖
• Other Authors: Hsiu-Hui Lee
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/01952874130263876556

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 96 === Face recognition is one important topic of biometric recognition. Face recognition has been researched for a long time, but there are kinds of factors that reduce the recognition rate such as illumination, pose, scale, orientation, facial expression, occlusion, etc. To improve the recognition rate, we need to avoid or to solve these factors. Though many researchers paid their efforts for solving these factors, there are no perfect solutions. In this thesis, we proposed an illumination compensation mechanism for the face recognition system. Furthermore, we solved scale and slight orientation factors by using eye detectors and avoided pose and heavy orientation factors by using a frontal face detector. There are four phases in our face recognition system: (1) face extraction (2) face normalization (3) face feature extraction (4) face classification model construction/ prediction. In face extraction phase, we extracted face by using an AdaBoost-based face detector and a skin-color detector. As for the normalization phase, we normalized three factors (scale, orientation, and illumination). We first detected the eyes for normalizing scale and orientation. Then we compensated illumination by our illumination compensation method. In feature extraction phase, we reduced the dimension by using PCA. In model construction/prediction phase, we trained the face classification model and predicted unknown face by using SVM. Finally, we evaluated our proposed illumination compensation method by the face databases, Yale Face Database B and Extended Yale Face Database B. And the experiment shows that our method yields higher face recognition rate than other methods under hard illumination conditions even when the lighting condition of training set is deficient.