2020/02/10 by Didan Deng, Deng, Didan, Zhaokang Chen +3
Computer Science · Engineering · Psychology · #Anomaly Detection Techniques and Applications #Audio and Speech Processing (eess.AS) #Computer Vision and Pattern Recognition (cs.CV) #Emotion and Mood Recognition #FOS: Computer and information sciences #FOS: Electrical engineering #Face and Expression Recognition #Multimedia (cs.MM) #cs.CV #cs.MM #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.03557
Accepted by FG2020
openalex publication_date 2020/02/10 · arxiv created 2020/03/10 · arxiv updated 2020/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We train a unified model to perform three tasks: facial action unit detection, expression classification, and valence-arousal estimation. We address two main challenges of learning the three tasks. First, most existing datasets are highly imbalanced. Second, most existing datasets do not contain labels for all three tasks. To tackle the first challenge, we apply data balancing techniques to experimental datasets. To tackle the second challenge, we propose an algorithm for the multitask model to learn from missing (incomplete) labels. This algorithm has two steps. We first train a teacher model to perform all three tasks, where each instance is trained by the ground truth label of its corresponding task. Secondly, we refer to the outputs of the teacher model as the soft labels. We use the soft labels and the ground truth to train the student model. We find that most of the student models outperform their teacher model on all the three tasks. Finally, we use model ensembling to boost performance further on the three tasks.