Cross-Domain Facial Expression Recognition via Disentangling Identity Representation
Authors: Tong Liu, Jing Li, Jia Wu, Lefei Zhang, Shanshan Zhao, Jun Chang, Jun Wan
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments with different settings on multiple benchmark datasets, and the results show that our method achieves superior performance compared with state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Tong Liu1 , Jing Li1 , Jia Wu2 , Lefei Zhang1 , Shanshan Zhao3 , Jun Chang1 and Jun Wan4 1Wuhan University, China 2Macquarie University, Sydney 3JD Explore Academy, China 4Zhongnan University of Economics and Law, China |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | JAFFE [Lyons et al., 1998]: The JAFFE (denoted as J) is a laboratory-controlled facial expression database that contains 213 samples from 10 Japanese females. Oulu-CASIA [Zhao et al., 2011]: The laboratory-controlled Oulu-CASIA (denoted as O) is a database of 2,880 image sequences from 80 subjects. RAF-DB [Li et al., 2017b]: The RAF-DB (denoted as R) is a real-world database consisting of around 30,000 facial images. SFEW 2.0 [Dhall et al., 2011]: The in-the-wild database SFEW 2.0 (denoted as S) is the benchmark dataset for the SReco sub-challenge in Emoti W 2015 [Dhall et al., 2015]. |
| Dataset Splits | Yes | We follow previous works [Chen et al., 2021] to use the entire dataset for the training and test sets. [...] In the experiment, we select images labeled with seven basic expressions, 12,271 of which are used for training and 3,068 for testing. [...] Specifically, we train our model using labeled images from multiple source domains and choose the best model on the validation splits of the training set. For testing, we evaluate the selected model on a retained target domain. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware specifications (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers. |
| Experiment Setup | No | While the paper describes the model architecture and loss functions, it does not provide specific hyperparameter values such as learning rate, batch size, or number of epochs needed for a reproducible experimental setup. |