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).