Face Recognition Using a Hybrid General Backpropagation Neural Network

In this paper, we propose two techniques for face recognition, namely, view-based and biometric-based face recognition. Both use general backpropagation neural networks (GBPN´s). In the view-based method, we extract sub-images of the eyes, the nose, and the mouth and feed them into a GBPN. In the biometric-based method, seven measurements of the face will be fed into another GBPN. We illustrate the results of the proposed algorithms by applying them on the Cambridge ORL face database, which contains quite a high degree of variability in expression, pose, and facial details. We have found that the view-based method outperforms the biometric-based method. Thus, we have selected the view-based method to function as the main neural network whereas the biometric-based method will function as a supportive neural network.