感性と感覚のセンシング 隠れマルコフモデルによる顔動画像からの表情認識

Abstract

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%.