title: Facial Action Unit Event Detection by Cascade of Tasks.
abstract: Automatic facial Action Unit (AU) detection from video is a long-standing problem in facial expression analysis. AU detection is typically posed as a classification problem between frames or segments of positive examples and negative ones, where existing work emphasizes the use of different features or classifiers. In this paper, we propose a method called Cascade of Tasks (CoT) that combines the use of different tasks (i.e., frame, segment and transition) for AU event detection. We train CoT in a sequential manner embracing diversity, which ensures robustness and generalization to unseen data. In addition to conventional frame-based metrics that evaluate frames independently, we propose a new event-based metric to evaluate detection performance at event-level. We show how the CoT method consistently outperforms state-of-the-art approaches in both frame-based and event-based metrics, across three public datasets that differ in complexity: CK+, FERA and RU-FACS.
authors.affiliation: School of Information Science and Engineering, Southeast University, Nanjing, China.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Ding
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: Jeffery F
authors.initials: JF
authors.lastname: Cohn
authors.affiliation: School of Information Science and Engineering, Southeast University, Nanjing, China.
authors.forename: Qiao
authors.initials: Q
authors.lastname: Wang