Human Face Detection In Visual Scenes Using Neural Networks

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This paper presents a neural network based face detection system. Our objective is to design a system that can detect human faces in visual scenes at high searching speed and accuracy. We used a neural network with a simple structure but trained using face and non-face samples preprocessed by several methods (position normalization, histogram equalization etc) to attain high accuracy, then pruned the size of the neural network so that it could run faster and reduced the total search area of a target visual scene using skin color detector. Skin color detection assumes that faces reside only in skin color regions. The system designed, is made up of two parts: the face detecting system (FDS) that detects the faces (made up of the face locator, the down sampler, and the merger), and the searching speed improving system (SIS). Speed improvement is achieved by reduction of the size of the face locator (FL) network using structural learning with knowledge and by reducing the face search area using skin color detection system (SCD). Faster training of the neural networks was also achieved using variable step sizes.

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