Joint Local-Global Discriminative Subspace Transfer Learning for Facial Expression Recognition

Traditional facial expression recognition (FER) has achieved satisfactory results to some extent, and most of the current methods are trained and evaluated on a single database. However, in real applications, the training and testing images are often collected in different scenarios, which will lead to performance degeneration. To tackle this problem, in this paper, we propose a novel transfer learning approach, named joint local-global discriminative subspace transfer learning (LGDSTL), for cross-database FER. In LGDSTL, first, we develop a joint local-global graph as the distance metric, in which we not only consider the local discriminative geometric structure for each database, but also consider a global graph to transfer knowledge. In this way, the discrepancy between the two databases will be significantly reduced. Then, we present a pairwise regression function to guide the discriminative subspace transfer learning. Additionally, a data reconstruction constraint is introduced to preserve the main discriminative information. Finally, comparative studies on six popular benchmarks demonstrate the effectiveness of the proposed approach.