title: Joint Patch and Multi-label Learning for Facial Action Unit Detection.
abstract: The face is one of the most powerful channel of nonverbal communication. The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS). FACS segments the visible effects of facial muscle activation into 30+ action units (AUs). AUs, which may occur alone and in thousands of combinations, can describe nearly all-possible facial expressions. Most existing methods for automatic AU detection treat the problem using one-vs-all classifiers and fail to exploit dependencies among AU and facial features. We introduce joint-patch and multi-label learning (JPML) to address these issues. JPML leverages group sparsity by selecting a sparse subset of facial patches while learning a multi-label classifier. In four of five comparisons on three diverse datasets, CK+, GFT, and BP4D, JPML produced the highest average F1 scores in comparison with state-of-the art.
authors.affiliation: School of Comm. and Info. Engineering, Beijing University of Posts and Telecom., Beijing China.
authors.forename: Kaili
authors.initials: K
authors.lastname: Zhao
authors.affiliation: Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
authors.forename: Wen-Sheng
authors.initials: WS
authors.lastname: Chu
authors.affiliation: Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
authors.forename: Fernando
authors.initials: F
authors.lastname: De la Torre
authors.affiliation: Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213; Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260.
authors.forename: Jeffrey F
authors.initials: JF
authors.lastname: Cohn
authors.affiliation: School of Comm. and Info. Engineering, Beijing University of Posts and Telecom., Beijing China.
authors.forename: Honggang
authors.initials: H
authors.lastname: Zhang