A View-Invariant Face Detection Method Based on Local PCA Cells

Abstract

This paper presents a view-invariant face detection method based on local PCA cells. In order to extract the general features of faces at each view and position, Gabor filters and local PCA (Principal Component Analysis) are used. Local PCA cells specialized for each view and position are made by applying the Gaussian to the outputs of local PCA of Gabor features. By applying the Gaussian, only local PCA cells having a view similar to an input give large values. This decreases an adverse influence of the local PCA cells of other views. As a result, only one classifier can treat multi-view faces well by integrating the outputs of local PCA cells. The effectiveness of the proposed method is confirmed by comparison with Support Vector Machine using Gaussian kernel and Radial Basis Function network. It is also confirmed that generalization ability is improved by selecting local PCA cells using the reconstruction error of local PCA.