Structure-Preserving Sparse Decomposition for Facial Expression Analysis

Abstract

Although facial expressions can be decomposed in terms of action units (AUs) as suggested by the facial action coding system, there have been only a few attempts that recognize expression using AUs and their composition rules. In this paper, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of AUs. First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition. Since domain experts' knowledge may not always be available for constructing an AU-dictionary, we also propose a structure-preserving dictionary learning algorithm, which we use to learn a structured dictionary as well as divide expressive faces into several semantic regions. Experimental results on publicly available expression data sets demonstrate the effectiveness of the proposed approach for facial expression analysis.