A novel family of subspace methods---protoface and its kernel version
In this paper, we present a novel feature extraction called the protoface. While the Eigenface is based on principal component analysis and the Fisherface on Fisher’s linear discriminant analysis, the protoface only requires decomposing the covariance matrix of the prototypes that can better describe whole observations. It is thus more computationally efficient especially when the number of the observations is large. Moreover, instead of using the method of multivariate analysis of variance, protoface relies on the analysis of distance. It is thus more appropriate in situations where the assumptions of normality of the observations are violated. Besides, by using the kernel trick, we also obtain a kernel protoface in the feature space defined by the kernel. Finally, we apply our methods to face recognition, and experimental results on both the AT&T and Yale databases are reported.