Showing papers on "Eigenface published in 1992"

Human face recognition method based on the statistical model of small sample size

Abstract: Automatic recognition of human faces is a frontier topic in computer vision. In this paper, a novel recognition approach to human faces is proposed, which is based on the statistical model in the optimal discriminant space. Singular value vector has been proposed to represent algebraic features of images. This kind of feature vector has some important properties of algebraic and geometric invariance, and insensitiveness to noise. Because singular value vector is usually of high dimensionality, and recognition model based on these feature vectors belongs to the problem of small sample size, which has not been solved completely, dimensionality compression of singular value vector is very necessary. In our method, an optimal discriminant transformation is constructed to transform an original space of singular value vector into a new space in which its dimensionality is significantly lower than that in the original space. Finally, a recognition model is established in the new space. Experimental results show that our method has very good recognition performance, and recognition accuracies of 100 percent are obtained for all 64 facial images of 8 classes of human faces.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Robust face identification scheme: KL expansion of an invariant feature space

Abstract: This paper proposes a new approach for extracting features from face images that offer robust face identification against image variations. We combine the K-L expansion technique with two new operations that transform the face pattern into an invariant feature space. The two operations are the affine transformation which yields a standard face view from the input face image, and its transformation into the Fourier spectrum domain, which develops the property of shift-invariance. Although the basic idea of applying the K-L expansion to extract features for face recognition originates from the eigenface approach proposed by Turk and Pentland our scheme offers superior performance due to the transformation into the invariant feature space. The performance of the two schemes for face identification against various imaging conditions is compared.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.