Automatic Facial Expression Recognition Using Statistical-Like Moments

Research in automatic facial expression recognition has permitted the development of systems discriminating between the six prototypical expressions, i.e. anger, disgust, fear, happiness, sadness and surprise, in frontal video sequences. Achieving high recognition rate often implies high computational costs that are not compatible with real time applications on limited-resource platforms. In order to have high recognition rate as well as computational efficiency, we propose an automatic facial expression recognition system using a set of novel features inspired by statistical moments. Such descriptors, named as statisticallike moments extract high order statistic from texture descriptors such as local binary patterns. The approach has been successfully tested on the second edition of Cohn-Kanade database, showing a computational advantage and achieving a performance recognition rate comparable than methods based on different descriptors.