The Spectral-Face Analysis for Face Recognition
Yan Zhu, Eric Sung · 2002
Subspace methods have been widely used for face recog-nition possibly because of their robustness and simplicity. Due to high dimensionality of image space, these methods are likely to encounter computational problem when hav-ing to deal with very large number of face training sam-ples. In this paper, a new subspace approach called the spectral-face analysis is developed to overcome this. It han-dles pixel information in matrix form rather than as vectors, and in so doing, keeps the basis computation invariant to the size of the training samples. Two types of statistics are im-plemented for the spectral-face analysis: the “covariance face ” and the “error face”. As they employ smaller vector space, the recognition rates are, as expected, not as good as conventional subspace methods such as PCA and LDA. To improve the performance, we extend the spectral-face methodology by some “stacking ” technique, the sole pur-pose being to increase the vector space dimension. Exten-sive tests have been carried out on the ORL face database with good and interesting results. Application of LDA to the spectral-face analysis also showed marked improvements. 1.