Expression Recognition Based on Low Pixel Face Images

Layer	Type	Kernel size /pixel×pixel	Stride	Output number	Output size /pixel×pixel
Layer 1	Convolution	3×3	1	64	30×30
Layer 2	Convolution	3×3	1	128	28×28
Layer 3	Max pooling	2×2	2	128	14×14
Layer 4	Convolution	3×3	1	128	14×14
Layer 5	Convolution	3×3	1	128	14×14
Layer 6	Convolution	3×3	1	256	12×12
Layer 7	Max pooling	2×2	2	256	6×6
Layer 8	Convolution	3×3	1	256	6×6
Layer 9	Convolution	3×3	1	256	6×6
Layer 10	Average pooling	3×3	3	256	2×2
Layer 11	Convolution	2×2	1	512	1×1
Layer 12	Fully connected	-	-	-	160×1
Output	SoftMax	-	-	-	8×1

Table 2. Facial expression dataset
View table
Table 2. Facial expression dataset
Category Angry Disgust Fearful Happy Sad Surprised Scorn Neutral
Train database 14040 12220 7800 14300 8970 8580 5460 8970
Test database 27 24 15 28 15 16 12 15

Table 3. Comparison of recognition accuracy and time
View table
Table 3. Comparison of recognition accuracy and time
Model Originaldatabase Local binarypatterndatabase Histogramequalizationdatabase
Accuracy /% 89.5 86.9 90.5
Time/s 1.54 1.27 1.56

Table 4. Improved recognition accuracy of CNN modelunit: %

View table

Table 4. Improved recognition accuracy of CNN modelunit: %

Category Accuracy	Angry	Disgust	Fearful	Happy	Sad	Surprised	Scorn	Neutral	Total
First experiment	96.3	91.7	60.0	100.0	86.7	100.0	83.3	93.3	90.5
Second experiment	88.9	91.7	86.7	100.0	80.0	100.0	83.3	93.3	91.2
Third experiment	96.3	95.8	60.0	100.0	86.7	93.8	75.0	93.3	89.8
Total	93.8	93.1	68.9	100.0	84.5	97.9	80.5	93.3	90.5

Table 5. Comparison of recognition accuracy and time between two models
View table
Table 5. Comparison of recognition accuracy and time between two models
Model LeNet-5 Without decision level
Accuracy /% 74.6 87.9
Time /s 0.59 0.31

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Fu Liu, Maojun Li, Jianwen Hu, Yuhe Xiao, Zhan Qi. Expression Recognition Based on Low Pixel Face Images[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101008

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