Cross-Domain Facial Expression Recognition Using Supervised Kernel Mean Matching

Even though facial expressions have universal meaning in communications, their appearances show a large amount of variation due to many factors, such as different image acquisition setups, different ages, genders, and cultural backgrounds etc. Collecting enough amounts of annotated samples for each target domain is impractical, this paper investigates the problem of facial expression recognition in the more challenging situation, where the training and testing samples are taken from different domains. To address this problem, after observing the fact of unsatisfactory performance of the Kernel Mean Matching (KMM) algorithm, we propose a supervised extension that matches the distributions in a class-to-class manner, called Supervised Kernel Mean Matching (SKMM). The new approach stands out by taking into consideration both matching the distributions and preserving the discriminative information between classes at the same time. The extensive experimental studies on four cross-dataset facial expression recognition tasks show promising improvements of the proposed method, in which a small number of labeled samples guide the matching process.