Implementation of skin color selection prior to Gabor filter and neural network to reduce execution time of face detection

This paper proposes a face detection system based on the skin color, the Gabor filter and the neural network. The use of Gabor filters and neural networks for face recognition is not new. However, the principal focus of the proposed paper is the implementation of skin color selection prior to Gabor filters and neural networks on order to reduce computation time. First, we analyze the skin color to extract skin areas which have an important probability to be faces. This technique robust to the lighting variation allows extracting, from an image, skin areas. We utilize this method to avoid wrong detection and to help the system detect the face in the right areas and minimize the research time. Second, to extract features, we propose a technique using the Gabor filter applied on the localized skin space. Finally, the vectors of the face features obtained by the Gabor filter are used as the input of a neural network classifier which classifies an input image pixel as a face or nonface pixel. Some results are shown to approve our approach efficiency.