Multiple experts for robust face authentication

Most of the multimodal authentication schemes currently developed, combine speech and image-based features together and benefit from the high performance offered by the speech modality. Depending on the application, speech data is not always available or cannot be used. This paper takes these cases into account and investigates the best performance that can be achieved by a system based on facial images only, using information taken from both profile and frontal views. Starting from two different profile-related modalities, one based on the profile shape, the other an the grey level distribution along this shape, we will issue a first profile-based expert whose performance is improved compared to each profile modality taken separately. A second expert will use the most invariant part of the frontal view, namely information from a rectangular grey level window centered around the eyes and nose features, in order to issue a frontal-based authentication. Different fusion schemes are studied and the best approach will be applied in order to efficiently combine our two experts. This will result in a robust image-based person authentication scheme that offers a success rate of 96.5% measured on the M2VTS multimodal face database.