Adaptive graph regularized transferable regression for facial expression recognition

Abstract

Facial expression recognition (FER) has tremendous potential in affective computing and human-computer interaction fields. Traditional FER algorithms usually perform the training and testing of models on a single domain. However, face images in real environments usually come from different domains, which greatly limits the performance of traditional algorithms. To handle this cross-domain recognition problem, we put forward a novel transfer learning model, called adaptive graph regularized transferable regression (AGTR), which can learn a discriminative projection matrix by embedding relaxed label regression, class sparsity structure, and adaptive graph structure into a unified framework. To be specific, in our method, we develop a relaxed label regression to learn a projection matrix. Then, we exploit a class sparsity structure in each class of samples separately to obtain a consistent subspace. Further, we design a novel adaptive graph structure, which can adaptively discover the geometric relationship between samples. Finally, we verify the advancement of our approach on four public facial expression databases.