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A Study on Face Image Recognition Using Feature Vectors  

Kim Jin-Sook (동의과학대학)
Kang Jin-Sook (동의과학대학)
Cha Eui-Young (동의과학대학)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.9, no.4, 2005 , pp. 897-904 More about this Journal
Abstract
Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.
Keywords
Face Recognition; Image Processing; PCA; LDA;
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1 Chellappa R., Wilson C., Sirohey S., 'Human and Machine Recognition of Faces: A Survey,' Proc. IEEE, Vol.83, no.5, pp.705-740, 1995
2 Belhumeur P.N., Hespanha J.P. and Kriegman D.J., 'Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.19, pp.711-720, 1997   DOI   ScienceOn
3 B. Moghaddam, C. Naster, A. Pentland, 'Bayesian Face Recognition Using Deformable Intensity Surfaces,' IEEE Conf. on Computer Vision & Pattern Recognition, June 1996
4 L. Sirovich, M. Kirby, 'Low-dimensional procedure for the characterization of human faces,' Journal of the Optical Society of America, 519-554, 1987
5 Fromherz T., Stucki P., Bichsel M., 'A Survey of Face Recognition,' MML Technical Report, No 97.01, Dept. of Computer Science, University of Zurich, 1997
6 Martinez A. M., Kak A. C., 'PCA versus LDA,' IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(2), pp.228-232, 2001   DOI   ScienceOn
7 Zhao W., Chellappa R., Rosefeld A., P.J. Phillips, 'Face recognition: A literature survey,' Technical report CAR-TR-948, Computer Vision Lab, University of Maryland, 2000
8 Hua Yu, Jie Yang, 'A Direct LDA Algorithm for High-Dimensional Data - with Application to Face Recognition,' Pattern Recognition 34(10), pp.2067-2070, 2001   DOI   ScienceOn
9 Turk M., Pentland A., 'Eigenfaces for recognition,' Journal of Cognitive Neuroscience, vol. 3, pp. 71-86, 1991   DOI   ScienceOn