Sensing of Feeling and "Kansei". Facial Expression Recognition from Image Sequence Using Hidden Markov Model.
Tatsumi Sakaguchi, Jun Ohya, Fumio Kishino · The Journal of The Institute of Image Information and Television Engineers · 1995
A method for recognizing facial expressions from time-sequential images by using Hidden Markov Models (HMM) is proposed. HMM has the advantage that it can process time-sequential infomation. Moreover we can expect the HMM to make generalizations from the training data because of its learning procedure. Each image of a facial expression is transformed into an image feature vector. Each element of the feature vector consists of the average power from a distinct frequency band obtained by applying the Wavelet transformation to the image. The sequence is converted into a symbol sequence by using a new category-separated vector quantization. The codebook is constructed by appending codewords selected from other categories to each category to reduce the probability of wrong symbolization for similar facial expressions. To recognize an observed sequence, the HMM that best matches the sequence is chosen, and the category of the HMM is the recognized expression. Experiments for recognizing 4 expressions result in a promising recognition rate of 93.7%.