From facial expression to level of interest: a spatio-temporal approach

This paper presents a novel approach to recognize the six universal facial expressions from visual data and use them to derive the level of interest using psychological evidences. The proposed approach relies on a two-step classification built on the top of refined optical flow computed from sequence of images. First, a bank of linear classifier was applied at frame level and the output of this stage was coalesced to produce a temporal signature for each observation. Second, temporal signatures thus computed from the training data set were used to train discrete hidden Markov models (HMMs) to learn the underlying models for each universal facial expressions. The average recognition rate of the proposed facial expression classifier is 90.9% without classifier fusion and 91.2% with fusion using a five fold cross validation scheme on a database of 488 video sequences that include 97 subjects. Recognized facial expressions were combined with the intensity of activity (motion) around the apex frame to measure the level of interest. To further illustrate the efficacy of the proposed approach two set of experiments, namely, television (TV) broadcast data (108 sequences of facial expression containing severe lighting conditions, diverse subjects and expressions) analysis and emotion elicitation on 21 subjects were conducted.