Pose invariant face recognition using Cellular Simultaneous Recurrent Networks

In this paper, we investigate two novel techniques based on Cellular Simultaneous Recurrent Network (CSRN) that can address the problem of small in-plane image rotation and large out-of-plane pose invariant face recognition in image sequences. In our first technique, for the first time in literature, we investigated the CSRNs for static image registration under affine transformations. Both the readily available CSRN with generalized multilayer perceptrons (GMLPs) and a modified MLP architecture with multi-layered feedback are implemented. Simulation results show that while both the GMLP network and our modified network are able to achieve localized image registration, our modified architecture is more effective in registering image pixels. In our second technique, we investigate the recognition problem for face image sequences with large pose variation as an implicit temporal prediction task for CSRN. CSRN is trained by image sequences to capture the temporal information. The Euclidian distances between successive frames of test and output image sequences indicate either a match or mismatch between the two corresponding face classes. We extensively evaluate our CSRN-based face recognition technique with 5 persons using publicly available VidTIMIT Audio-Video face dataset. In order to verify the performance of CSRN, we also implement Elman neural network for comparison.