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Real Time Face Detection and Recognition using Rectangular Feature Based Classifier and PCA-based MLNN  

Kim, Jong-Min (조선대학교 일반대학원 전산통계학과)
Lee, Kee-Jun (광주보건대학 보건교육정보과)
Publication Information
Journal of Digital Contents Society / v.11, no.4, 2010 , pp. 417-424 More about this Journal
Abstract
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 pre-processing 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.
Keywords
Principal Component Analysis (PCA); Multi-Layer Neural Networks (MLNN);
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