A neural network approach to face/palm recognition

This paper proposes a face/palm recognition system based on decision-based neural networks (DBNN). The face recognition system consists of three modules. First, the face detector finds the location of a human face in an image. The eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth. (Eye-glasses will be permissible.) Lastly, the third module is a face recognizer. The DBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates its successful application to face recognition applications on both the public (FERET) and in-house (SCR) databases. In terms of speed, given the extracted features, the training phase for 100-200 persons would take less than one hour on Sparc10. The whole recognition process (including eye localization, feature extraction, and classification using DBNN) may consume only a fraction of a second on Sparc10. Experiments on three different databases all demonstrated high recognition accuracies. A preliminary study also confirms that a similar DBNN recognizer can effectively recognize palms, which could potentially offer a much more reliable biometric feature.