A Saccadic Vision System for Landmark Detection and Face Recognition

Abstract

We present a new approach to the problem of face (non-rigid object) recognition. We introduce a novel methodology that exploits the advantages offered by active vision architectures, and utilizes highly compressed feature representations for person identification. Specifically, we describe a unified model of low-level visual attention that combines purely data-driven processes with primitive object recognition mechanisms and model-based reasoning, and show that such process can form the foundations of a high performance face recognition system. The described architecture employs a number of independent, parallel visual routines responsible for object localization, identification, and scene interpretation, corresponding to the “where” and “what” channels of visual perception. The model is biologically plausible and is motivated by processing strategies in the human visual system (HVS). To test the validity of the described face recognition architecture, a number of experiments were carried out on a large and varied face database (FERET). The active vision components were used to detect faces by locating their individual facial landmarks, and to derive a compact face code invariant to changes in viewing geometry and imaging conditions. Simulation results on 100 subjects (216 images) demonstrated that both the “where” and “what” channels perform with high accuracy and their combined performance reached 100% in detecting all relevant facial landmarks. The identification experiments achieved 89.6% accuracy for the match task and reached 100% for surveillance. These results indicate that the proposed mechanisms are capable of efficiently locating and encoding information relevant for all aspects of face recognition.