Detecting faces in color images

We propose in this work a method for detecting faces in color images with complex backgrounds. The approach starts with the transformation of the image pixels from the RGB color space to the chrominance space (YCbCr). Secondly, a Gaussian model is fitted on the transformed image in order to calculate the likelihood of skin for each pixel and to create a likelihood image. Thirdly, by thresholding the likelihood image, skin pixels are segmented to form a binary skin map, which contains the candidate face regions. Finally, a verification process is carried out to determine whether these candidate face regions are real faces or not.