Synthesis and Recognition of Face Profiles

Research on biometrical systems and especially on face recognition systems has become of high interest. Nowadays several approaches exist to recognize frontal views of faces. Under certain constraints the recognition accuracy even for huge databases seems to be acceptable. However, it has shown, that the recognition performance of nearly all state-of-the-art systems dramatically drops down, when faces rotated in-depth or even profile views are presented. In this work we present our actual experiments and results of neural network based approaches for face profile recognition systems. One of the main challenges is to implement a self learning approach that does not need any direct 3D information. The key idea of our approaches is to derive the correspondences between head profiles and frontal views by examples automatically. With this approach we are able to synthesize profile views of heads by presenting frontal views. The quality of the resulting systems can be measured with Hidden Markov Models (HMM) and an extension to the well-known Eigenfaces approach. The performances are evaluated on the MUGSHOT and the FERET database.