Facial Action Unit Event Detection by Cascade of Tasks
Xiaoyu Ding, Wen-Sheng Chu, Fernando De La Torre, Jeffery F. Cohn, Qiao Wang; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2400-2407
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 framebased 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 RUFACS.
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bibtex]
@InProceedings{Ding_2013_ICCV,
author = {Ding, Xiaoyu and Chu, Wen-Sheng and De La Torre, Fernando and Cohn, Jeffery F. and Wang, Qiao},
title = {Facial Action Unit Event Detection by Cascade of Tasks},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}