Model-based human face detection in unconstrained scenes

In this paper, we present a model-based face detection approach for determining the region occupied by upright frontal views of human faces in unconstrained natural scenes without any priori information. The process is composed of one offline and two online steps. The first step is offline and consists of the modeling stage that generates a model describing the "face" and "non-face" patterns by only few clusters. The second step is the pattern matching stage, which matches small patterns cropped from the input image at different positions and scales against the modeled clusters. The third step is the classification stage, which identifies the specified pattern as face or non-face using the nearest neighbor classifier. The main advantage of the proposed approach is the reduced computation complexity with respect to previously reported systems. This is done by an intelligent arbitration strategy that avoids the exhaustive search at different scales. The system is tested on 50 images containing 253 faces. The correct detection rate is approximately 83% while the false detection count is low.