Discovering Domain-Invariant Subspace for Depression Recognition by Jointly Exploiting Appearance and Dynamics Feature Representations

Abstract

Focusing on cross-dataset Automated Depression Recognition (ADR) by jointly exploring facial appearance and dynamics feature representations, we explore to propose a novel Latent Domain Adaptation Depression Recognition (latDADR) framework via discovering a discriminative domain-invariant subspace. In this subspace, the between-domain distribution discrepancy would be semantically minimized, and meanwhile the within-domain geometric structures would also be discriminatively preserved. In latDADR, we respectively optimize two target classifiers on dynamics features and appearance features as well as learn a source classifier on appearance features, and then encode certain shared components of the different domain classifiers as low-rank and sparse regularization terms. Moreover, the prediction results from two target classifiers are constrained to be consistent for better fusing the discriminative information from different feature representations. We specially use the ${l_{2,1}}$ -norm based loss function for learning robust classifiers on different feature representations. Different from the state-of-the-arts, our method can borrow the discriminative information from another auxiliary domain for ADR, even if the target prior information is very scarce and the features of the source and target domains are partially different but overlapping. The proposed method is evaluated on three depression databases, and the experimental results demonstrate the superiorities and outstanding performance compared with several representative works.