Facial Expression Recognition Based on Local Feature Fusion of Convolutional Neural Network

Lisha Yao; Guoming Xu; Feng Zhao

doi:10.3788/LOP57.041513

Laser & Optoelectronics Progress, Volume. 57, Issue 4, 041513(2020)

Facial Expression Recognition Based on Local Feature Fusion of Convolutional Neural Network

Lisha Yao, Guoming Xu^*, and Feng Zhao

Institute of Information and Software, Institute of Information Engineering, Anhui Xinhua University, Hefei, Anhui 230088, China

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Figures & Tables(9)

Fig. 1. Model structure of CNN

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Fig. 2. Diagram of mixing pool process

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Fig. 3. Flowchart of expression recognition framework

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Fig. 4. Average recognition rates of different algorithms for 7 kinds of facial expressions

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Fig. 5. Average recognition rates of different decision-level fusion methods for 7 kinds of facial expressions

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Table 1. Parameter setting of CNN
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Table 1. Parameter setting of CNN
Layer Structure design of CNN
Input 32×32
C1 5×5 conv, 32
S1 2×2,max-pooling & mean-pooling
C2 5×5 conv, 6
C3 5×5 conv, 128
S2 2×2,max-pooling & mean-pooling
Dropout Dropout
FC 120
Output Center loss & Softmax

Table 2. Average expression recognition rate on CK + database%

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Table 2. Average expression recognition rate on CK + database%

Expression	Anger	Disgust	Fear	Happy	Neutral	Sad	Surprise
Anger	95.02	0.82	0.86	0.71	0.74	0.83	1.02
Disgust	1.56	94.14	0.95	0.52	1.17	0.82	0.84
Fear	0.81	0.83	94.77	0.61	1.02	0.82	1.14
Happy	0.72	0.75	0.65	95.66	1.01	0.63	0.58
Neutral	1.16	1.35	1.27	1.47	92.61	1.12	1.02
Sad	0.61	1.11	1.61	0.33	1.70	93.98	0.66
Surprise	0.69	0.72	0.81	0.53	0.92	0.62	95.71

Table 3. Average expression recognition rate on JAFFE database %

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Table 3. Average expression recognition rate on JAFFE database %

Expression	Anger	Disgust	Fear	Happy	Neutral	Sad	Surprise
Anger	100	0	0	0	0	0	0
Disgust	1.03	96.66	0.43	0.37	0.62	0.52	0.37
Fear	0.38	0.49	96.89	0.36	0.73	0.43	0.72
Happy	1.22	1.32	1.21	92.37	1.61	0.88	1.39
Neutral	0	0	0	0	100	0	0
Sad	0.35	0.66	0.81	0.36	0.68	96.88	0.26
Surprise	0.51	0.56	0.66	0.36	0.71	0.42	96.78

Table 4. Comprehensive performance comparison of different algorithms
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Table 4. Comprehensive performance comparison of different algorithms
Algorithm Recognition rate /% Recognition time /ms
Ref.[5] 92.10 1690
Ref.[12] 94.17 2773
Ref.[13] 89.01 2246
Ref.[14] 92.06 2655
Proposed method 94.56% 2685

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Lisha Yao, Guoming Xu, Feng Zhao. Facial Expression Recognition Based on Local Feature Fusion of Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041513

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