Survey on Facial Expression Recognition: History, Applications, and Challenges
By Xibin Zhao, Junjie Zhu, Bingjun Luo, Yue Gao. 2021
18 citations recorded by the source corpus.
Facial expressions are most important channels for transmitting affective information in human interaction. In the early 1970s, due to the limitation of weak computing power and powerless algorithms, automatic facial expression recognition (AFER) entered the freezing period for a long time. However, with the geometrical growth of computing power and the rapid improvement of algorithms, the accuracy of AFER has been significantly enhanced. FER has ushered in the flowering and fruitful application in different fields such as medical treatment, transportation, and business. This article introduces and surveys these recent advances. We first provide a detailed review of the related studies in FER, including emotion representation, well-known datasets, and FER’s history. Next, we detailed the background and practical significance of FER technology’s application in different fields. We finally summarize some of the scientific and engineering challenges to promote better use of FER in real-world applications.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
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Citations in the corpus
- Online Continual Learning with Contrastive Vision Transformer (2022, background): “Since then, a lot of effort has been dedicated to improving Vision Transformers’ data efficiency and model efficiency [92,32,91,40], where an effective direction is to strategically integrate properties of convolution into the Transformer architecture [73,91,48,84,26,94,23,17].”