Facial Action Unit Event Detection by Cascade of Tasks

• Published in: 2013 IEEE International Conference on Computer Vision Vol. 2013; pp. 2400 - 2407
• Main Authors: Ding, Xiaoyu, Chu, Wen-Sheng, De La Torre, Fernando, Cohn, Jeffery F., Wang, Qiao
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 2013
• Subjects: Cascades; Computer vision; Conferences; Detectors; Facial; Feature extraction; Frames; Gold; Measurement; Robustness; Segments; Support vector machines; Tasks; Training; Trains
• ISSN: 1550-5499
• DOI: 10.1109/ICCV.2013.298

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.