A novel bidirectional neural network for face recognition

The recognition of face images is a complicated problem. Face images are often sufferedfrom variations in brightness, head rotation, facial emotions and so on. Besides, amazing abilities of human brain in face recognition in the presence of these variations, contribute to design face recognition systems based on procedure of human brain. Surveying the recognition and perceptual system of human, shows that, this system has hierarchical and bidirectional structure. Furthermore, the performance of the system would strongly be improved by applying the information of upper layers of face recognition system in interpreting and processing the input data. In this paper, novel bidirectional architecturefor face recognition inspired by human face recognition system is presentedvia applying inversion in artificial neural networks (ANN´s). In this approach, storeddata in the inverse networkis applied in the recognition system iterativelyandthen the correctness of face recognition model has been consequently improved by 8%. The proposed model is able to produce 12 various facial expressions on the output, from only one input expressionof each person, after training with AUT database images.