Face detection using the 1st-order RCE classifier

We present a new face detection algorithm based on the 1st-order reduced Coulomb energy (RCE) classifier. The algorithm locates frontal views of human faces at any degree of rotation and scale in complex scenes. The face candidates and their orientations are first determined by computing the Hausdorff distance between a simple face abstraction model and binarized test windows in an image pyramid. Then, after normalizing the energy, each face candidate is verified by two subsequent classifiers; a binary image classifier and the 1st-order RCE classifier While the binary image classifier is employed as a pre-classifier to discard nonfaces with minimum computational complexity, the 1st-order RCE classifier is used as the main face classifier for final verification. An optimal training method to construct the representative face model database is also presented. Experimental results show that the proposed algorithm yields a high detection ratio, while yielding no false alarm.