A Study of Face Recognition using the PCA and Error Back-Propagation
In this paper the real-time face region was detected by suggesting the rectangular feature-based classifier and the robust detection algorithm that satisfied the efficiency of computation and detection performance was suggested. By using the detected face region as a recognition input image, in this paper the face recognition method combined with PCA and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a preprocessing algorithm of input face image, this method computes the eigenface through PCA and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the face recognition is performed by inputting the multi-layer neural network. As a result of comparing with existing methods, Euclidean and Mahananobis method, the suggested method showed the improved recognition performance with the incorrect matching or matching failure. In addition, by studying the changes of recognition rate according to the learning rate in various environments, the most optimum value of learning rate was calculated.
Component Principal Component Analysis(PCA) Multi-Layer Neural Networks(MLNN)
Jong-Min Kim Myung-A Kang
Computer Science and Statistic Graduate School Chosun University GwangJu,Korea Dept.of Computer Engineering GwangJu University GwangJu,Korea
国际会议
南京
英文
580-583
2010-08-26（万方平台首次上网日期，不代表论文的发表时间）