Subject-dependent degrees of reliability to solve a face recognition problem using multiple neural networks

The interest towards biometric approach to identity verification is high, because of the need to protect everything that could have a value for some purpose. Face recognition is one of these biometric techniques, having its greater advantage in requiring a limited interaction by user. We present a Face Recognition System (FRS) based on multiple neural networks using a belief revision mechanism. Each network is associated to an a-priori reliability value for each identity stored in database, modelling the specific skill of the modules composing the system with the recognition of a given subject. Every time a network is in conflict with the global response, it is forced to retrain itself, subjecting the system to a continuous learning. The main goal of this work is to carry out some preliminary tests to evaluate accuracy and robustness of FRS with “subject-dependent” reliability values, when some changes can affect the considered features. Tests over digitally aged faces are also conducted.