MLPBoost: A combined AdaBoost / multi-layer perceptron network approach for face detection

Face detection is a research area in computer vision of great interest. Even though several different methods have been developed, improvements can still be made in the false-positive detection and increase in the speed of the detector. In this work, we investigate the AdaBoost technique as an artificial neural network. We propose a new model called MLPBoost, which is an hybridization between AdaBoost and Multi-Layer Perceptron (MLP) networks. This algorithm has shown improvements in the performance of classifiers already trained with AdaBoost, either by the increase in the detection rate and the reduction of false positive rates, or by decreasing the processing time of these classifiers.