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Face Recognition using Vector Quantizer in Eigenspace  

임동철 (아주대학교 전자공학부)
이행세 (아주대학교 전자공학부)
최태영 (아주대학교 전자공학부)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.41, no.5, 2004 , pp. 185-192 More about this Journal
Abstract
This paper presents face recognition using vector quantization in the eigenspace of the faces. The existing eigenface method is not enough for representing the variations of faces. For making up for its defects, the proposed method use a clustering of feature vectors by vector quantization in eigenspace of the faces. In the trainning stage, the face images are transformed the points in the eigenspace by eigeface(eigenvetor) and we represent a set of points for each people as the centroids of vector quantizer. In the recognition stage, the vector quantizer finds the centroid having the minimum quantization error between feature vector of input image and centriods of database. The experiments are performed by 600 faces in Faces94 database. The existing eigenface method has minimum 64 miss-recognition and the proposed method has minimum 20 miss-recognition when we use 4 codevectors. In conclusion, the proposed method is a effective method that improves recognition rate through overcoming the variation of faces.
Keywords
vector quantization; face recognition; eigenface; mixture model;
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