An evolutionary feature-based visual attention model applied to face recognition

Visual attention is a powerful mechanism that enables perception to focus on a small subset of the information picked up by our eyes. It is directly related to the accuracy of an object categorization task. In this paper we adopt those biological hypotheses and propose an evolutionary visual attention model applied to the face recognition problem. The model is composed by three levels: the attentive level that determines where to look by means of a retinal ganglion network simulated using a network of bi-stable neurons and controlled by an evolutionary process; the preprocessing level that analyses and process the information from the retinal ganglion network; and the associative level that uses a neural network to associate the visual stimuli with the face of a particular person. To test the accuracy of the model a benchmark of faces is used.