Biologically-Inspired Face Detection: Non-Brute-Force-Search Approach

Abstract

We present a biologically-inspired face detection system. The system applies notions such as saliency, gist, and gaze to localize a face without performing blind spatial search. The saliency model consists of highly parallel low-level computations that operate in domains such as intensity, orientation, and color. It is used to direct attention to a set of conspicuous locations in an image as starting points. The gist model, computed in parallel with the saliency model, estimates holistic image characteristics such as dominant contours and magnitude in high and low spatial frequency bands. We are limiting its use to predicting the likely head size based on the entire scene. Also, instead of identifying face as a single entity, this system performs detection by parts and uses spatial configuration constraints to be robust against occlusion and perspective.