Face detection and identification using a hierarchical feed-forward recognition architecture

We apply a hierarchical feed-forward neural architecture to the problem of face recognition. The network is similar to the neocognitron-approach and a two-layer variation of this architecture, which has previously been successfully applied to patch classification tasks. We extend this architecture to a three-layer one, which allows not only identification of image patches, but also detection in larger images. In the research area of face recognition, a lot of expertise has been developed for the problem of either identification or detection, but approaches which deal with both problems simultaneously are rarely to be found. In this work, we apply the hierarchical approach to this problem and evaluate the performance on artificial datasets.