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Genetic algorithms based adaptive search area control for real time multiple face detection using neural networks

Fast and automatic face detection from visual scenes is a vital preprocessing step in many face applications like recognition, authentication, analysis, etc. While detection of a single face can be accomplished with good accuracy, multiple faces detection in real time is more challenging not only because of different face sizes and orientations, but also due to limits of the processing power available. In this paper, we propose a real time multiple face detection method using multiple neural networks and an adaptive search area control method base on genetic algorithms. Although, neural networks and genetic algorithms may not be suitable for real time application because of their long processing times, we show that high detection accuracies and fast speeds can be achieved using small sized effective neural networks and a genetic algorithm with a small population size that requires few generations to converge. The proposed method subdivides the face into several small regions, each connected to an individual neural network. The subdivision guarantees small size networks and presents the ability to learn different face regions features using region-specialized input coding methods. The genetic algorithm is used during the real time search to extract possible face samples from face candidates. The fitness of the face samples is calculated using the neural networks. In the successive frames, the search area is adaptively controlled based on the information inherited from the proceeding frames. To prove the effectiveness of our approach we performed real time simulation using an inexpensive USB camera.