Dual-graph regularized discriminative transfer sparse coding for facial expression recognition

Abstract Facial expression recognition has recently received an increasing attention due to its great potentiality in real world applications. Conventional facial expression recognition is often conducted on the assumption that training data and testing data are obtained from the same dataset. However, in reality, the data are often collected from different devices or environments, which will severely degrade the recognition performance. To tackle this problem, in this paper, we investigate the cross-dataset facial expression recognition problem, and propose a novel dual-graph regularized transfer sparse coding method (DGTSC). Specifically, aiming to reduce the distribution divergence of different databases while preserving the geometrical structures, we construct a dual-graph, by defining the inter-domain and intra-domain similarity, to measure the distance between different databases. Moreover, we further present a dual-graph regularized discriminative transfer sparse coding method (DGDTSC), which exploits the label information, to make our model has more discriminative power. Extensive experimental results and analysis on several facial expression datasets show the feasibility and effectiveness of the proposed methods.