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Face Recognition using Eigenfaces and Fuzzy Neural Networks  

김재협 (한양대학교 컴퓨터공학과)
문영식 (한양대학교 컴퓨터공학과)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.41, no.3, 2004 , pp. 27-36 More about this Journal
Abstract
Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.
Keywords
face recognition; eigenfaces; fuzzy; neural networks;
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Times Cited By KSCI : 1  (Citation Analysis)
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