Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates

Shiro Kumano, Kazuhiro Otsuka, Junji Yamato, Eisaku Maeda, Yoichi Sato

Abstract: In this paper, we propose a method for pose-invariant facial expression recognition from monocular video sequences. The advantage of our method is that, unlike existing methods, our method uses a very simple model, called the variable-intensity template, for describing different facial expressions, making it possible to prepare a model for each person with very little time and effort. Variable-intensity templates describe how the intensity of multiple points defined in the vicinity of facial parts varies for different facial expressions. By using this model in the framework of a particle filter, our method is capable of estimating facial poses and expressions simultaneously. Experiments demonstrate the effectiveness of our method. A recognition rate of over 90% was achieved for horizontal facial orientations on a range of ±40 degrees from the frontal view.

External IDs:dblp:conf/accv/KumanoOYMS07