Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation

• Published in: IEEE transactions on image processing Vol. 25; no. 5; pp. 1977 - 1992
• Main Authors: Yimo Guo, Guoying Zhao, Pietikainen, Matti
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-05-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Diffeomorphic Growth Model; Dynamic Facial Expression Recognition; Dynamics; Face recognition; Feature extraction; Groupwise Registration; Image processing; Image recognition; Image registration; Mathematical models; Object recognition; Representations; Shape; Sociology; Sparse Representation; Statistics; Dynamic facial expression recognition; diffeomorphic growth model; groupwise registration; sparse representation
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2016.2537215

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.