Cross-Database Facial Expression Recognition via Unsupervised Domain Adaptive Dictionary Learning

Abstract

Dictionary learning based methods have achieved state-of-the-art performance in the task of conventional facial expression recognition (FER), where the distributions between training and testing data are implicitly assumed to be matched. But in the practical scenes this assumption is usually broken, especially when testing samples and training samples come from different databases, a.k.a. the cross-database FER problem. To address this problem, we propose a novel method called unsupervised domain adaptive dictionary learning (UDADL) to deal with the unsupervised case that all samples in target database are completely unlabeled. In UDADL, to obtain more robust representations of facial expressions and to reduce the time complexity in training and testing phases, we introduce a dual dictionary pair consisting of a synthesis one and an analysis one to mutually bridge the samples and their codes. Meanwhile, to relieve the distribution disparity of source and target samples, we further integrate the learning of unlabeled testing data into UDADL to adaptively adjust the misaligned distribution in an embedded space, where geometric structures of both domains are also encourage to be preserved. The UDADL model can be solved by an iterate optimization strategy with each sub-optimization in a closed analytic form. The extensive experiments on Multi-PIE and BU-3DFE databases demonstrate that the proposed UDADL is superior over most widely-used domain adaptation methods in dealing with cross-database FER, and achieves the state-of-the-art performance.