A Novel LDA and HMM-Based Technique for Emotion Recognition from Facial Expressions

Over the last few years, many researchers have done a lot of work on emotion recognition from facial expressions using the techniques of image processing and computer vision. In this paper we explore the application of Latent Dirichlet Allocation, a technique conventionally used in Natural text processing, when used with Hidden Markov Model, for the same. The classification is done at an image sequence level. Each frame of an image sequence is represented by a feature vector, which is mapped to one of the words from the dictionary generated using K-means. Latent Dirichlet Allocation then models each image sequence as a set of topics. We further know the order of topics for image sequence from the order of words, which we use for classification in the next step. This is done by training a Hidden Markov Model for each emotion. The emotions dealt with are six basic emotions: happy, fear, sad, surprise, angry, disgust and contempt. We compare our results with another technique in which sequence information of words instead of topics is used by HMM for learning facial expression dynamics. The results have been presented on CK+ dataset [2]. The accuracy obtained on the proposed technique is 80.77% .The use of word-sequence in found to give better results in general.