| dc.description.abstract | In this thesis, we present a component-based face recognition method using the facial block feature to increase the recognition rate. In the complex lighting environment, if the system takes the whole facial images for recognition, the skin area will be extremely easy to be influenced by the lighting changes so as to decrease the recognition rate. To remedy this problem, we propose a method using the facial components images instead of the whole facial images for recognition.
Firstly, the Active Appearance Model (AAM) is adopted to detect six facial components images, which are left eyebrow, right eyebrow, left eye, right eye, nose and mouth, respectively. Then, Principal Component Analysis (PCA) is utilized to calculate the desired feature vectors and decrease the dimension of the original feature vectors. After that, the K-means algorithm is employed to cluster these feature vectors. The Support Vector Machine (SVM) is utilized to train different recognition modules using the information of the clusters. Finally, the result of recognition is decided by each recognition module using the voting method.
The experimental image database contains the case of complex light changes, which includes two different sources of lights and the variations of light directions from fluorescent lamps and desk lamps. When the training images include all of light changes, the recognition rate of EigenFace is 94% and the proposed method can be up to 96%. If the testing images are with complex lighting changes than the training ones, the recognition rates of EigenFace and the proposed method are 31% and 63% , respectively. Obviously, the proposed method can increase the recognition rate by using the proposed facial components. In the training stage, the proposed method is more robust without special light changes images. Compared with EigenFace, the recognition rate of the proposed method reveals the great improvement. Experimental results show that the proposed method can indeed achieve reliable performance in face recognition. | en_US |