Transfer subspace learning for cross-dataset facial expression recognition

In this paper, we propose a transfer subspace learning approach cross-dataset facial expression recognition. To our best knowledge, this problem has been seldom addressed in the literature. While many facial expression recognition methods have been proposed in recent years, most of them assume that face images in the training and testing sets are collected under the same conditions so that they are independently and identically distributed. In many real applications, this assumption does not hold as the testing data are usually collected online and are generally more uncontrollable than the training data. Hence, the testing samples are likely different from the training samples. In this paper, we define this problem as cross-dataset facial expression recognition as the training and testing data are considered to be collected from different datasets due to different acquisition conditions. To address this, we propose a transfer subspace learning approach to learn a feature subspace which transfers the knowledge gained from the source domain (training samples) to the target domain (testing samples) to improve the recognition performance. To better exploit more complementary information for multiple feature representations of face images, we develop a multi-view transfer subspace learning approach where multiple different yet related subspaces are learned to transfer information from the source domain to the target domain. Experimental results are presented to demonstrate the efficacy of these proposed methods for the cross-dataset facial expression recognition task.