A transfer learning approach to cross-database facial expression recognition

Despite the high facial expression recognition accuracy reported on individual databases, cross-database facial expression recognition is still a challenging problem. This is essentially a problem of generalizing a facial expression recognizer trained with data of certain subjects under certain conditions to different subjects and/or different conditions. Such generalization capability is crucial in real-world applications. However, little attention has been focused on this problem in the literature. Transfer learning, a domain adaptation approach, provides effective techniques for transferring knowledge from source (training) data to target (testing) data when they are characterized by different properties. This paper makes the first attempt to apply transferring learning to cross-database facial expression recognition. It proposes a transfer learning based cross-database facial expression recognition approach, in which two training stages are involved: One for learning knowledge from source data, and the other for adapting the learned knowledge to target data. This approach has been implemented based on Gabor features extracted from facial images, regression tree classifiers, the AdaBoosting algorithm, and support vector machines. Evaluation experiments have been done on the JAFFE, FEED, and extended Cohn-Kanade databases. The results demonstrate that using the proposed transferring learning approach the cross-database facial expression recognition accuracy can be improved by more than 20%.