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- W4328012097 abstract "Facial expression recognition has always been a difficult problem in the field of computer vision. In recent years, with the rapid development of deep learning, some methods based on semi-supervised learning have greatly improved the accuracy of facial expression recognition. For the semi-supervised learning algorithm, it can use both labeled and unlabeled samples during the training process, and select some unlabeled data to expand the data set for model training. In order to make full use of the information in the expression image, this paper proposes a semi-supervised deep learning expression recognition algorithms embedded with Squeeze-and-Excitation attention mechanism(SSA-SE). By extracting the expression features better, it shows a good model expression ability. In this paper, the experimental evaluation is carried out on the public data RAF-DB, and the accuracy of 86.72 % is achieved under the condition of only 4000 label samples. Experimental results on other datasets also show the effectiveness of the method. Compared with previous studies, this method has advantages in expression recognition task." @default.
- W4328012097 created "2023-03-22" @default.
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- W4328012097 date "2022-12-09" @default.
- W4328012097 modified "2023-09-30" @default.
- W4328012097 title "Semi-supervised Deep Facial Expression Recognition with Embedded Attention Mechanism" @default.
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- W4328012097 doi "https://doi.org/10.1109/iccc56324.2022.10065941" @default.
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