Sima Taheri , University of Maryland, College Park
Vishal M. Patel , University Of Maryland, College Park
Rama Chellappa , University of Maryland, College Park
Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. In this paper, we propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm which benefits from the idea of sparsity and morphological diversity. This entails building data-driven dictionaries for neutral and expressive components. The DCS algorithm then uses these dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition. Experiments on publicly available expression and face datasets show the effectiveness of our method.
Face and gesture recognition, Computer vision, Facial expression, Nonverbal signals, Affect sensing and analysis, Affective Computing
S. Taheri, V. M. Patel and R. Chellappa, "Component-based Recognition of Faces and Facial Expressions," in IEEE Transactions on Affective Computing.