An Attention Model-Based Facial Expression Recognition Algorithm

Table 1. Effect of hyper-parameters on network performance
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Table 1. Effect of hyper-parameters on network performance
Variable Value Accuracy /%
1 97.35
d 4 97.45
8 97.25
8 95.72
r 16 97.45
32 95.41

Table 2. Effect of attention model location on network performance
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Table 2. Effect of attention model location on network performance
Dataset Location Accuracy /%
After conv 96.64
CK+ Before pooling 97.45
After pooling 95.72
After conv 72.69
MMI Before pooling 74.73
After pooling 72.59

Table 3. Performance comparison of different expression recognition methods

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Table 3. Performance comparison of different expression recognition methods

Method	Experimentalsetting	Accuracy /%
Method	Experimentalsetting	CK+	MMI
3DCNN^[32]	Sequence-based	85.90	53.20
LBP-TOP^[33]	Sequence-based	88.99	59.51
HOG 3D^[34]	Sequence-based	91.44	60.89
STM-ExpLet^[35]	Sequence-based	94.19	75.12
DTGAN^[18]	Sequence-based	97.25	-
Island Loss^[25]	Image-based	94.39	74.68
IACNN^[22]	Image-based	95.37	71.55
DLPCNN^[24]	Image-based	95.78	-
DeRL^[36]	Image-based	97.30	73.23
Ref.[19]	Image-based	97.37	-
PPDN^[37]	Image-based	97.30	-
VGG16(ours)	Image-based	91.72	64.13
ResNet5(ours)	Image-based	86.87	57.09
CSACNN(ours)	Image-based	97.45	74.73

Table 4. Performance comparison of different modules
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Table 4. Performance comparison of different modules
Model Accuracy /%
CK+ MMI
Base 94.73 67.61
Base+CA 95.21 71.47
Base+SA 95.62 70.17
Base+Crop 95.82 71.41
Base+CA+SA 96.43 72.98
Base+CA+SA+Crop 97.45 74.73

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Jinghui Chu, Wenhao Tang, Shan Zhang, Wei Lü. An Attention Model-Based Facial Expression Recognition Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121015

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