Suppressing Uncertainties for Large-Scale Facial Expression Recognition

By Kai-Hong Wang, Xiaojiang Peng, Jianfei Yang, Shijian Lu, Yu Qiao. Computer Vision and Pattern Recognition, 2020

634 citations recorded by the source corpus.

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties suspend the progress of large-scale Facial Expression Recognition (FER) in data-driven deep learning era. To address this problelm, this paper proposes to suppress the uncertainties by a simple yet efficient Self-Cure Network (SCN). Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over FER dataset to weight each sample in training with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with \textbf{88.14}\% on RAF-DB, \textbf{60.23}\% on AffectNet, and \textbf{89.35}\% on FERPlus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/92006a84036f9e37e11c773c19141ad8a6675e39

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Guo-qing Guo
2. Fuyan Ma
3. Feng Zhou
4. Shutao Li
5. Zengqun Zhao
6. Bin Sun
7. Qiangchang Wang
8. Fanglei Xue

In Computer Vision and Pattern Recognition

1. Guo-qing Guo
2. Fuyan Ma
3. Feng Zhou
4. Shutao Li
5. Zengqun Zhao
6. Bin Sun
7. Qiangchang Wang
8. Fanglei Xue

In Experimental and Cognitive Psychology

1. Fuyan Ma
2. Feng Zhou
3. Shutao Li
4. Zengqun Zhao
5. Bin Sun
6. Qingshan Liu

Citations in the corpus

• Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition (2021, neutral): “EfficientFace [51] 1.28 0.15 SCN [49] 11.18 1.82 RAN [48] 11.19 14.55 PSR[50] 20.24 10.12 DACL [7] 103”
• Robust Lightweight Facial Expression Recognition Network with Label Distribution Training (2021, methodology): “2020b) and SCN (Wang et al. 2020a), an oversampling strategy 1 is employed.”
• Facial Expression Recognition With Visual Transformers and Attentional Selective Fusion (2021, methodology): “based methods including CSLD [15], ResNet+VGG [57], SHCNN [58], LDR [59], as well as two recent state-of-the-art methods(RAN [10], SCN [11]).”
• TransFER: Learning Relation-aware Facial Expression Representations with Transformers (2021, background): “FERPlus PLD [3] 85.10 RAN [28] 88.55 SeNet50 [2] 88.80 RAN-VGG16 [28] 89.16 SCN [27] 89.35 KTN [17] 90.49 TransFER (Ours) 90.83 Tabel 6 compares the performance of our TransFER with the state-of-the-art methods on FERPlus.”