title: Human face detection in visual scenes using neural networks
abstract: 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 the skin color detector. Skin color detection assumes that faces reside only in skin color regions. The system design is made up of two parts: the face detecting system that detects the faces, and the searching speed improving system. Speed improvement is achieved by reducing the face locator network size using the structural learning with knowledge and by reducing the face search area using the skin color detection system. Faster training of the neural networks was also achieved using variable step sizes
keyphrases: visual scene neural network face detecting system skin color detector backpropagation self organizing maps down-sampling human face recognition merging overlapping detections