Simultaneous recognition of facial expression and identity via sparse representation
Automatic recognition of facial expression and facial identity from visual data are two challenging problems that are tied together. In the past decade, researchers have mostly tried to solve these two problems separately to come up with face identification systems that are expression-independent and facial expressions recognition systems that are person-independent. This paper presents a new framework using sparse representation for simultaneous recognition of facial expression and identity. Our framework is based on the assumption that any facial appearance is a sparse combination of identities and expressions (i.e., one identity and one expression). Our experimental results using the CK+ and MMI face datasets show that the proposed approach outperforms methods that conduct face identification and face recognition individually.