View-invariant face detection method based on local PCA cells

The 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 are used. Local PCA cells specialized to each view and position are made by applying a Gaussian to the outputs of the local PCA of Gabor features. By applying the Gaussian, only the local PCA cells which are a similar view to an input give large values. This decreases the bad 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. It is confirmed that the proposed method can detect multi-view faces. Generalization ability is improved by selecting the local PCA cells using a reconstruction error of local PCA.