One shot emotion scores for facial emotion recognition

Facial emotion recognition in unconstrained settings is a difficult task. They key problems are that people express their emotions in ways that are different from other people, and, for large datasets, there are not enough examples of a specific person to model his/her emotion. A model for predicting emotions will not generalize well to predicting the emotions of a person who has not been encountered during the training. We propose a system that addresses these issues by matching a face video to references of emotion. It does not require examples from the person in the video being queried. We compute the matching scores without requiring fine registration. The method is called one-shot emotion score. We improve classification rate of interdataset experiments over a baseline system by 23% when training on MMI and testing on CK+.