Facial expression recognition through adaptive learning of local motion descriptor

A novel bag-of-words based approach is proposed for recognizing facial expressions corresponding to each of the six basic prototypic emotions from a video sequence. Each video sequence is represented as a specific combination of local (in spatio-temporal scale) motion patterns. These local motion patterns are captured in motion descriptors (MDs) which are unique combinations of optical flow and image gradient. These MDs can be compared to the words in the bag-of-words setting. Generally, the key-words in the wordbook as reported in the literature, are rigid, i.e., are taken as it is from the training data and cannot generalize well. We propose a novel adaptive learning technique for the key-words. The adapted key-MDs better represent the local motion patterns of the videos and generalize well to the unseen data and thus give better expression recognition accuracy. To test the efficiency of the proposed approach, we have experimented extensively on three well known datasets. We have also compared the results with existing state-of-the-art expression descriptors. Our method gives better accuracy. The proposed approach have been able to reduce the training time including the time for feature-extraction more than nine times and test time more than twice as compared to current state-of-the-art descriptor.