Statistical Analysis of Human Facial Expressions

Abstract

This paper presents a method for generalizing human facial expressions by means of a statistical analysis of human facial expressions coming from various per- sons. The data used for the statistical analysis are obtained by tracking a generic facial wireframe model in video sequences depicting the formation of the different human fa- cial expressions, starting from a neutral state. Wireframe node tracking is performed by a pyramidal variant of the well-known Kanade-Lucas-Tomasi (KLT) tracker. The loss of tracked features is handled through a model deformation procedure that increases the robustness of the tracking algorithm. Tracking initialization is performed in a semi- automatic fashion, i.e., the facial wireframe model is fitted to an image representing a neutral facial expression, exploiting physics-based deformable shape modeling. The dy- namic facial expression output model is MPEG-4 compliant. The method has been tested on a variety of sequences with very good results, including a database of video sequences representing human faces changing from the neutral state to the one that represents a fully formed human facial expression.