Emotion Recognition from Facial Expressions using Multilevel HMM

Human-computer intelligent interaction (HCII) is an emerging field of science aimed at providing natural ways for humans to use computers as aids. It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. The most expressive way humans display emotions is through facial expressions. This work focuses on automatic facial expression recognition from live video input using temporal cues. Methods for using temporal information have been extensively explored for speech recognition applications. Among these methods are template matching using dynamic programming methods and hidden Markov models (HMM). This work exploits existing methods and proposes a new architecture of HMMs for automatically segmenting and recognizing human facial expression from video sequences. The novelty of this architecture is that both segmentation and recognition of the facial expressions are done automatically using a multilevel HMM architecture while increasing the discrimination power between the different classes. In the work we explore person-dependent and person-independent recognition of expressions.