A Region Group Adaptive Attention Model For Subtle Expression Recognition

Article Properties
  • DOI (url)
    10.1109/taffc.2021.3133429
  • Publication Date
    2023/04/01
  • Journal
    IEEE Transactions on Affective Computing
  • Indian UGC (journal)
  • Refrences
    57
  • Citations
    1
  • Gan Chen School of Computer Engineering and Science, Shanghai University, Shanghai, China ORCID (unauthenticated)
  • Junjie Peng School of Computer Engineering and Science & the Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China ORCID (unauthenticated)
  • Wenqiang Zhang Academy for Engineering and Technology, and School of Computer Science and Technology, Fudan University, Shanghai, China ORCID (unauthenticated)
  • Kanrun Huang Nauto, Inc., Palo Alto, CA, USA
  • Feng Cheng Hasso Plattner Institute, Postsdam, Germany
  • Haochen Yuan School of Computer Engineering and Science, Shanghai University, Shanghai, China
  • Yansong Huang School of Computer Engineering and Science, Shanghai University, Shanghai, China ORCID (unauthenticated)
Cite
Chen, Gan, et al. “A Region Group Adaptive Attention Model For Subtle Expression Recognition”. IEEE Transactions on Affective Computing, vol. 14, no. 2, 2023, pp. 1613-26, https://doi.org/10.1109/taffc.2021.3133429.
Chen, G., Peng, J., Zhang, W., Huang, K., Cheng, F., Yuan, H., & Huang, Y. (2023). A Region Group Adaptive Attention Model For Subtle Expression Recognition. IEEE Transactions on Affective Computing, 14(2), 1613-1626. https://doi.org/10.1109/taffc.2021.3133429
Chen, Gan, Junjie Peng, Wenqiang Zhang, Kanrun Huang, Feng Cheng, Haochen Yuan, and Yansong Huang. “A Region Group Adaptive Attention Model For Subtle Expression Recognition”. IEEE Transactions on Affective Computing 14, no. 2 (2023): 1613-26. https://doi.org/10.1109/taffc.2021.3133429.
Chen G, Peng J, Zhang W, Huang K, Cheng F, Yuan H, et al. A Region Group Adaptive Attention Model For Subtle Expression Recognition. IEEE Transactions on Affective Computing. 2023;14(2):1613-26.
Refrences
Title Journal Journal Categories Citations Publication Date
Facial expression recognition using ROI-KNN deep convolutional neural networks Acta Automatica Sinica 2016
DLIB-ML: A machine learning toolkit 2009
Contour and region harmonic features for sub-local facial expression recognition 2020
Real-time facial expression recognition “in the wild 2020
FERAtt: Facial expression recognition with attention net 2019
Citations
Title Journal Journal Categories Citations Publication Date
Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey IEEE Transactions on Instrumentation and Measurement
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Science: Mathematics: Instruments and machines
  • Technology: Engineering (General). Civil engineering (General)
 2023
Citations Analysis
Category Category Repetition
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
Science: Mathematics: Instruments and machines1
Technology: Engineering (General). Civil engineering (General)1
The category Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey and was published in 2023. The most recent citation comes from a 2023 study titled Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey. This article reached its peak citation in 2023, with 1 citations. It has been cited in 1 different journals. Among related journals, the IEEE Transactions on Instrumentation and Measurement cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year