Real-time face detection using Gentle AdaBoost algorithm and nesting cascade structure

In this paper, a face detector based on Gentle AdaBoost algorithm and nesting cascade structure is proposed. Nesting cascade structure is introduced to avoid that too many weak classifiers in a cascade classifier will slow down the face detection speed of this cascade classifier. Gentle AdaBoost algorithm is used to train node classifiers on a Haar-like feature set to improve the generalization ability of the node classifier. Consequently, the face detection performance of the face detector is improved. Experimental results have proved that the proposed algorithm can significantly reduce the number of weak classifiers, increase the detection speed, and slightly raise the detection accuracy as well. On the CIF (352×288) video sequences, the average detection speed of the proposed face detector can achieve 125fps, which is superior to the state-of-the-art face detectors and completely satisfies the demand of real-time face detection.