Dynamic Facial Expression Recognition Using Fuzzy Hidden Markov Models

Humans are immersed in a high-tech computing environment. Dependence on the pervasiveness of modern devices encompasses even simple everyday tasks. Unfortunately, the rapidly increasing expectations on the intelligence of these devices often exceeds their abilities. Devices are expected to perceive their environment, understand our intent, and autonomously carry out appropriate tasks. This paper presents an approach to bring device perception one step closer to meeting the high user expectations. A novel application of fuzzy hidden Markov models to automatically identify dynamic facial expressions is proposed in a human-device interaction context. A low-complexity vision based facial feature tracker is integrated with a hidden Markov model adapted to take advantage of fuzzy measures. Benefits over traditional hidden Markov mode Is for facial expression recognition include a reduction in training time, and improved flexibility for use as a valuable part of a larger multimodal system for natural human-device interaction