Facial expression recognition with auto-illumination correction

Past researchers have shown maximum recognition rates given the static images and techniques to recognize face and related features of the face. Yet, these research though contribute and motivate greatly to building effective future systems, fail to address the temporal dynamics of the face to enhance the system´s training. In this paper, analyzing the facial expression on a given face from an image automatically and produces the result stating the emotion on the subject´s face. Face is recognized using the skin and chrominance of the extracted image and the image is cropped. Expressions on the face are determined using the localization of points called Action Units (AUs) internally without labeling them. Though AUs are found to be effective, most expressions on the face have shown to overlap these points thereby curbing the recognition. Using a mapping technique, the extracted eyes and mouth are mapped together. Illumination on an image plays a vital role in highlighting the portrait and therefore is a barrier when extracting the facial features. This is a delimiter while analyzing the face. This limitation is removed and automatically corrected using a Color Constancy Algorithm with minkowski norms. The experimental results show better face detection rate under variable luminance levels. The system was tested against a collection of faces both containing single face images and multiple faces in a scene. We achieved a recognition rate of 60% when detecting in a multiple face image.