Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition

Xinyi, Zou,; Yan, Yan; Xue, Jing-Hao; Chen, Si; Wang, Hanzi

doi:10.1007/978-3-031-19800-7_40

Cited by 7 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, the main objective of GTL is to extract latent common factors by preserving the statistical properties of different domains and further optimizing them using the geometric structure of each domain to mitigate negative transfer effects. The CDNet [31] presents a novel approach for recognizing complex facial expressions across databases with only a limited number of camera angles. By employing a cascaded decomposition network, the CDNet effectively handles various scenarios of facial expressions and achieves more accurate recognition performance in cross-domain learning tasks.…”

Section: Related Workmentioning

confidence: 99%

“…The existing transfer learning methods for cross-database facial expression recognition primarily encompass TCA [24], GFK [25], SA [26], DANN [27], DSAN [28], CDNet [31], and MRAN [18]. In this paper, we propose the MJDDAN model, which is compared with the above methods under identical experimental conditions.…”

Section: Cross-database Facial Expression Recognition Experiments And...mentioning

confidence: 99%

See 1 more Smart Citation

Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition

Yan,

Yue,

et al. 2024

Electronics

View full text Add to dashboard Cite

In order to obtain more fine-grained information from multiple sub-feature spaces for domain adaptation, this paper proposes a novel multi-representation joint dynamic domain adaptation network (MJDDAN) and applies it to achieve cross-database facial expression recognition. The MJDDAN uses a hybrid structure to extract multi-representation features and maps the original facial expression features into multiple sub-feature spaces, aligning the expression features of the source domain and target domain in multiple sub-feature spaces from different angles to extract features more comprehensively. Moreover, the MJDDAN proposes the Joint Dynamic Maximum Mean Difference (JD-MMD) model to reduce the difference in feature distribution between different subdomains by simultaneously minimizing the maximum mean difference and local maximum mean difference in each substructure. Three databases, including eNTERFACE, FABO, and RAVDESS, are used to design a large number of cross-database transfer learning facial expression recognition experiments. The accuracy of emotion recognition experiments with eNTERFACE, FABO, and RAVDESS as target domains reach 53.64%, 43.66%, and 35.87%, respectively. Compared to the best comparison method chosen in this article, the accuracy rates were improved by 1.79%, 0.85%, and 1.02%, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Cross-database Facial Expression Recognition Experiments And...mentioning

confidence: 99%

Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition

Yan,

Yue,

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Zhang et al [ 27 ] proposed the use of “uncertainty” learning to quantify the degree of “uncertainty” for various noise problems in facial expressions, mixing “uncertainty” features from different faces to separate noise and expression features. Zou et al [ 28 ] regarded expressions as a weighted sum of different types of expressions and learned basic expression features through a sequential decomposition mechanism. Fan et al [ 29 ] designed a two-stage training program to further recognize expressions using identity information.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition Using Local Sliding Window Attention

Qiu

Zhao

et al. 2023

Sensors

View full text Add to dashboard Cite

There are problems associated with facial expression recognition (FER), such as facial occlusion and head pose variations. These two problems lead to incomplete facial information in images, making feature extraction extremely difficult. Most current methods use prior knowledge or fixed-size patches to perform local cropping, thereby enhancing the ability to acquire fine-grained features. However, the former requires extra data processing work and is prone to errors; the latter destroys the integrity of local features. In this paper, we propose a local Sliding Window Attention Network (SWA-Net) for FER. Specifically, we propose a sliding window strategy for feature-level cropping, which preserves the integrity of local features and does not require complex preprocessing. Moreover, the local feature enhancement module mines fine-grained features with intraclass semantics through a multiscale depth network. The adaptive local feature selection module is introduced to prompt the model to find more essential local features. Extensive experiments demonstrate that our SWA-Net model achieves a comparable performance to that of state-of-the-art methods with scores of 90.03% on RAF-DB, 89.22% on FERPlus, 63.97% on AffectNet.

show abstract

Few-shot learning for facial expression recognition: a comprehensive survey

Kim

2023

J Real-Time Image Proc

View full text Add to dashboard Cite

Facial expression recognition (FER) is utilized in various fields that analyze facial expressions. FER is attracting increasing attention for its role in improving the convenience in human life. It is widely applied in human–computer interaction tasks. However, recently, FER tasks have encountered certain data and training issues. To address these issues in FER, few-shot learning (FSL) has been researched as a new approach. In this paper, we focus on analyzing FER techniques based on FSL and consider the computational complexity and processing time in these models. FSL has been researched as it can solve the problems of training with few datasets and generalizing in a wild-environmental condition. Based on our analysis, we describe certain existing challenges in the use of FSL in FER systems and suggest research directions to resolve these issues. FER using FSL can be time efficient and reduce the complexity in many other real-time processing tasks and is an important area for further research.

show abstract

Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition

Cited by 7 publications

References 28 publications

Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition

Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition

Facial Expression Recognition Using Local Sliding Window Attention

Few-shot learning for facial expression recognition: a comprehensive survey

Contact Info

Product

Resources

About