Facial expression analysis by integrating information of feature-point positions and gray levels of facial images

For image analysis of facial expressions, we deal with information which are not only the gray levels of pixels but also positions of feature points. We first obtain each result of facial expression identification by using each information and then show that there is the difference between their results. Due to their difference, we integrate both information to improve the results. We define integrated similarity measures by linear combination or belief integration using virtual belief space and show the experimental results of facial expression identification using the measure.