Expression Recognition Based on Attention-Split Convolutional Residual Network

Layer	Output	Proposed network
	44×44	7×7，64，s=1
	44×44	3×3 maxpool，s=1
1	44×44	$[\begin{array}{l} S P C o n v, 64 \\ S P C o n v, 64 \\ C A \end{array}] \times 2$
2	22×22	$[\begin{array}{l} S P C o n v, 128 \\ S P C o n v, 128 \\ C A \end{array}] \times 2$
3	11×11	$[\begin{array}{l} S P C o n v, 256 \\ S P C o n v, 256 \\ C A \end{array}] \times 2$
4	6×6	$[\begin{array}{l} S P C o n v, 512 \\ S P C o n v, 512 \\ C A \end{array}] \times 2$
	1×1	Global avgpool 7‑d fc

Table 2. Model performance
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Table 2. Model performance
Model name Number of parameters Model size /Mbit Accuracy in FER2013 /% Accuracy in RAF-DB /%
ResNet18 11180103 42.7 69.769 81.845
SPResNet18 4188487 16.2 72.444 83.638
SPResNet18_CA 4263223 16.5 72.666 84.420

Table 3. Comparative experiment on FER2013
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Table 3. Comparative experiment on FER2013
Data NetWork Accuracy /%
FER2013 SHCNN^［1］ 69.1
LCMA^［21］ 69.6
LIANG^［22］ 70.3
Zhou^［23］ 70.9
SPResNet18_CA 72.6

Table 4. Comparative experiment on RAF-DB
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Table 4. Comparative experiment on RAF-DB
Data NetWork Accuracy /%
RAF-DB DLP-CNN^［24］ 74.20
EAU-Net^［25］ 81.83
DeepExp3D^［26］ 82.06
pACNN^［27］ 83.05
SPResNet18_CA 84.42

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Jiamin Chen, Yang Xu. Expression Recognition Based on Attention-Split Convolutional Residual Network[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815009

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