Laser & Optoelectronics Progress, Volume. 58, Issue 6, 610005(2021)
Facial Expression Recognition by Merging Multilayer Features of Lightweight Convolutional Networks
Figures & Tables(9)
Table 1. Parameters of the CNN
View tableTable 1. Parameters of the CNN
Layer name c s t Output size Conv2D 1 (3×3) 16 2 56×56×16 Bottleneck_M1 16 1 1 56×56×16 Bottleneck_M2 24 2 5 28×28×24 Bottleneck_M3 24 1 5 28×28×24 Bottleneck_M3_1 32 1 5 28×28×32 Bottleneck_M3_2 32 1 5 28×28×32 Feature selection module 1 32 Bottleneck_M4 32 2 5 14×14×32 Bottleneck_M5 32 1 5 14×14×32 Feature selection module 2 32 Bottleneck_M6 40 1 5 14×14×40 Bottleneck_M7 40 1 5 14×14×40 Feature selection module 3 40 Bottleneck_M8 40 1 5 14×14×40 Bottleneck_M9 48 2 5 7×7×48 Bottleneck_M10 64 1 5 7×7×64 Conv2D 2 (1×1) 64 1 7×7×64 Global average pooling 64 Concat 168 Reshape 1 1×1×168 Dropout 1×1×168 Conv2D 3 (1×1) k 1×1×k Softmax 1×1×k Reshape 2 k
Table 2. Number of categories in AffectNet dataset after random screening
View tableTable 2. Number of categories in AffectNet dataset after random screening
Class Neutral Happy Sad Surprise Fear Disgust Angry Contempt Total Train set 5978 5979 5966 5963 6378 3803 5979 3750 43796 Test set 500 500 500 500 500 500 500 500 4000
Table 3. Performance of different models
View tableTable 3. Performance of different models
Model name Number of parameters Model size/Mbit Acc in RAF-DB/% Acc in AffectNet/% base_model_R 195159 2.9 83.64 56.93 base_model_M 195159 2.9 84.23 57.30 ms_model_fully 3299303 40.2 85.45 57.37 ms_model_v1_R 193927 3.0 84.81 57.43 ms_model_v1_M 193927 3.0 85.49 57.70
Table 4. Accuracy of different methods in RAF-DB and AffectNet datasets
View tableTable 4. Accuracy of different methods in RAF-DB and AffectNet datasets
Method Acc in RAF-DB/% Acc in AffectNet/% Boosting-POOF[ 24 ]73.19 VGG16[ 25 ]80.96 51.11 DLP-CNN[ 22 ]84.13 pACNN[ 26 ]83.05 55.33 gACNN[ 26 ]85.07 58.78 E2-CapsNet[ 27 ]85.24 ms_model_v1_M 85.49 57.70
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Shen Hao, Meng Qinghao, Liu Yinbo. Facial Expression Recognition by Merging Multilayer Features of Lightweight Convolutional Networks[J]. Laser & Optoelectronics Progress, 2021, 58(6): 610005
Paper Information
Category: Image Processing
Received: Jun. 30, 2020
Accepted: --
Published Online: Mar. 11, 2021
The Author Email: Yinbo Liu (liuyinbo@tju.edu.cn)
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