Face detection by color and multilayer feedforward neural network

In this paper, we introduce a novel approach for automatic detection of human faces embedded in dissimilar lighting. The proposed system consists of two primary parts. The first part is to convert the input RGB color images to a binary image directly using color segmentation. Because the absolute values of r, g, and b are totally different with the various skin colors in the altered lighting conditions and the relative value between r, g, and b are almost similar with the different skin colors in changed brightness circumstances, we use the relative value between r, g, and b in the color segmentation process to binarize the RGB color images directly instead of "color images to gray level images, then binary ones". For this reason, our system is very robust for different lighting conditions. The second part of the proposed system is to search the potential face regions and perform the task of face detection. In the second part, each face candidate is obtained from the isosceles-triangle criterion that is based on the rules of "the combination of two eyes and one mouth", and then to be normalized to a standard size (60*60 pixels). Next, each of these normalized potential face regions are fed to neural networks function to obtain the location of the face region. The proposed face detection system can detect color multiple faces embedded in dissimilar lighting conditions. Moreover, it can conquer different size, varying pose and expression. Experimental results demonstrate that an approximately 97% success rate is achieved and the relative false estimation rate is very low.