RETRACTED CHAPTER: An Efficient Channel Attention CNN for Facial Expression Recognition

Wang, Xingwei; Duan, Haiqiang; Chen, Wei

doi:10.1007/978-981-16-6554-7_8

Cited by 1 publication

(2 citation statements)

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“…The extraction sequence numbers of 72 facial key points (see Fig. 2b) are: 19,22,23,26,39,37,44,46,28,30,49,51,53,55,59 and 57. The serial numbers of the IPs generated (see Fig.…”

Section: Generation Methods Of 24 Ibsmentioning

confidence: 99%

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Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of Key Regions

Zhu,

Li,

Liu

et al. 2023

CMC

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Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs and uncropped IBs. To alleviate the overfitting problem in training, we first extract the basic features of the preprocessed sequence through the transfer learning layer, and then extract the global and local spatial features of the output data through the GIA layer and the BIA layer, respectively. In the BIA layer, the input data will be cropped into local feature vectors with attention weights to extract the local features of the ME frames; in the GIA layer, the global features of the ME frames will be extracted. Finally, after fusing the global and local feature vectors, the ME time-series information is extracted by Bi-LSTM. The experimental results show that using IBs can significantly improve the model's ability to extract subtle facial features, and the model works best when 10 IBs are used.

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“…The extraction sequence numbers of 72 facial key points (see Fig. 2b) are: 19,22,23,26,39,37,44,46,28,30,49,51,53,55,59 and 57. The serial numbers of the IPs generated (see Fig.…”

Section: Generation Methods Of 24 Ibsmentioning

confidence: 99%

“…To address the shortcomings of short duration and low action intensity in MER, we add an attention mechanism in a CNN [23]. This design makes the CNN model not only extract the features of the whole face but also focus on the changes in local details.…”

Section: Neural Network With Attention Mechanismmentioning

confidence: 99%