Robust Lightweight Facial Expression Recognition Network with Label Distribution Training
Authors: Zengqun Zhao, Qingshan Liu, Feng Zhou3510-3519
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on realistic occlusion and pose variation datasets demonstrate that the proposed Efficient Face is robust under occlusion and pose variation conditions. Moreover, the proposed method achieves state-of-the-art results on RAF-DB, CAER-S, and Affect Net-7 datasets with accuracies of 88.36%, 85.87%, and 63.70%, respectively, and a comparable result on the Affect Net-8 dataset with an accuracy of 59.89%. |
| Researcher Affiliation | Academia | Zengqun Zhao, Qingshan Liu* and Feng Zhou B-DAT Lab, Nanjing University of Information Science & Technology, Nanjing, China {zqzhao, qsliu}@nuist.edu.cn |
| Pseudocode | No | No. The paper describes architectural components and the training process in text and refers to Figure 3 for overall structure, but does not include any pseudocode or formal algorithm blocks. |
| Open Source Code | Yes | The code and training logs are available at https://github.com/zengqunzhao/Efficient Face. |
| Open Datasets | Yes | To verify the effectiveness of the proposed method, we conduct the experiments on three popular in-the-wild facial expression datasets: RAF-DB (Li and Deng 2018), CAER-S (Lee et al. 2019), and Affect Net (Mollahosseini, Hasani, and Mahoor 2017), and five realistic occlusion and pose variation datasets: FED-RO (Li et al. 2019c), Occlusion Affect Net, Occlusion-RAF-DB, Pose-Affect Net and Pose RAF-DB (Wang et al. 2020b). |
| Dataset Splits | No | No. The paper provides explicit training and testing sample counts for RAF-DB, CAER-S, and Affect Net, but it does not explicitly provide numerical details for a separate validation split for these main datasets. |
| Hardware Specification | Yes | All the models are trained on the NVIDIA Ge Force Titan Xp GPU based on the open-source Py Torch (Paszke et al. 2019) platform. |
| Software Dependencies | No | No. The paper mentions 'Py Torch (Paszke et al. 2019) platform' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For Efficient Face, parameters were optimized via the SGD optimizer with an initial learning rate of 0.1 and a mini-batch size of 128. |