A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image processing and applications
Color image processing and applications
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
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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.