Invariant face recognition using fringe-adjusted joint-transform-correlator-based neural network

In this paper, we propose an optoelectronic fringe-adjusted joint transform correlator (JTC) based two-layer neural network for invariant face recognition while accommodating in-plane and out-of-plane 3D distortions. The neural network is utilized in the training stage for a sequence of facial images and through a process of supervised learning in order to create composite images that are invariant to 3D distortions. The proposed technique is implemented by using the FJTC technique. The FJTC technique has been chosen due to its superior performance over alternate JTCs and the feasibility of its implementation in the all-optical domain. The simulation results obtained from the proposed technique are then compared with those obtained using alternate techniques (such as using synthetic discriminant functions). The fringe-adjusted JTC based neural network technique has been found to be more efficient and yields better results than the synthetic discriminant function based technique.