Improved Classroom Face Recognition Algorithm Based on InsightFace and Its Application

Xichu Tian; Hansong Su; Gaohua Liu; Tengteng Liu

doi:10.3788/LOP57.221501

Laser & Optoelectronics Progress, Volume. 57, Issue 22, 221501(2020)

Improved Classroom Face Recognition Algorithm Based on InsightFace and Its Application

Xichu Tian, Hansong Su, Gaohua Liu^*, and Tengteng Liu

Author Affiliations

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

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Figures & Tables(17)

Fig. 1. Mobile-Block structure with stride of 1.(a) MobileFaceNet; (b) Dual-MobileFaceNet

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Fig. 2. Mobile-Block structure with stride of 2. (a) MobileFaceNet; (b) Dual-MobileFaceNet

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Fig. 3. Schematic of Dual-MobileFaceNet structure

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Fig. 4. Schematic of double classifier structure

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Fig. 5. Examples of self-made training dataset

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Fig. 6. Classroom scene. (a) Real scene; (b) sketch map

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Fig. 7. Interface connection of Jetson TX2

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Fig. 8. Recognition results of proposed algorithm. (a) 8-people video; (b)16-people video

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Fig. 9. Recognition accuracy confusion matrix of 8-people video. (a) InsightFace; (b) Double classifier

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Fig. 10. Diagram of different face sizes. (a) Big face; (b) medium face; (c) small face

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Fig. 11. Recognition accuracy of different networks for different sizes of faces

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Fig. 12. Recognition accuracy of different algorithms for different sizes of faces

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Table 1. Network structure of Dual-MobileFaceNet

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Table 1. Network structure of Dual-MobileFaceNet

Input size/Numberof channels	Type	Output size/Numberof channels	Operation	s	n	Pad
112×112/3	Convolution	56×56/64	3×3 Conv	2	1	1
56×56/64	Convolution	56×56/64	3×3 dw_Conv	1	1	1
56×56/64	Dual-Block	56×56/128+2k	$[\begin{array}{l} 1 \times 1 pw_Conv \\ 3 \times 3 dw_Conv \\ 1 \times 1 Linear_pw_Conv \end{array}]$	1	2
56×56/128+2k	Mobile-Block	28×28/64	$[\begin{array}{l} 1 \times 1 pw_Conv \\ 3 \times 3 dw_Conv \\ 1 \times 1 Linear_pw_Conv \end{array}]$	2	1	1
28×28/64	Dual-Block	28×28/128+6k	$[\begin{array}{l} 1 \times 1 pw_Conv \\ 3 \times 3 dw_Conv \\ 1 \times 1 Linear_pw_Conv \end{array}]$	1	6
28×28/128+6k	Mobile-Block	14×14/128	$[\begin{array}{l} 1 \times 1 pw_Conv \\ 3 \times 3 dw_Conv \\ 1 \times 1 Linear_pw_Conv \end{array}]$	2	1	1
14×14/128	Dual-Block	14×14/256+4k	$[\begin{array}{l} 1 \times 1 pw_Conv \\ 3 \times 3 dw_Conv \\ 1 \times 1 Linear_pw_Conv \end{array}]$	1	4
14×14/256+4k	Mobile-Block	7×7/128	$[\begin{array}{l} 1 \times 1 pw_Conv \\ 3 \times 3 dw_Conv \\ 1 \times 1 Linear_pw_Conv \end{array}]$	2	1	1
7×7/128	Dual-Block	7×7/256+2k	$[\begin{array}{l} 1 \times 1 pw_Conv \\ 3 \times 3 dw_Conv \\ 1 \times 1 Linear_pw_Conv \end{array}]$	1	2
7×7/256+2k	Convolution	7×7/512	1×1 pw_Conv	1	1	0
7×7/512	Convolution	1×1/512	7×7 Linear_Conv	1	1	0
1×1/512	Convolution	1×1/128	1×1 Linear_pw_Conv	1	1	0

Table 2. Comparison of experiment results of different networks

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Table 2. Comparison of experiment results of different networks

Network	Recognition accuracy /%					Speed /(frame·s^-1)	Model Size /MB
Network	AgeDB	CFP_FP	CFP_FF	LFW	CALFW	Speed /(frame·s^-1)	Model Size /MB
ResNet-101^[13]	97.28	95.11	99.65	99.71	96.65	42.64	250
ResNet-50^[13]	96.03	94.06	99.62	99.52	95.36	70.84	174.5
DenseNet-201(k=32)^[12]	96.68	94.83	99.62	99.68	96.04	100.17	161.8
DenseNet-169(k=32)^[12]	95.38	93.66	99.01	98.86	95.28	120.34	114.4
ShuffleNet(1×,g=3)^[22]	89.27	89.09	97.75	98.70	93.06	410.78	7.4
MobileNet-v1^[20]	88.65	88.54	97.06	98.43	93.01	206.64	13.7
MobileNet-v2^[21]	88.81	88.53	97.36	98.38	92.88	230.71	8.6
MobileFaceNet^[11]	92.95	89.46	98.03	98.96	93.89	432.41	4.1
Dual-MobileFaceNet	93.94	91.16	98.68	99.18	94.02	326.35	8.8

Table 3. Recognition accuracy comparison of different algorithms%

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Table 3. Recognition accuracy comparison of different algorithms%

Algorithm	LFW	CFP-FP	AgeDB-30
DeepFace^[3]	95.53	87.46	89.61
Deep FR^[23]	96.04	88.26	90.13
DeepID2^[4]	96.14	87.85	90.26
FaceNet^[5]	96.95	88.20	90.69
SphereFace^[6]	97.58	90.03	91.84
CosFace^[7]	98.43	90.75	92.33
InsightFace^[8]	99.18	91.16	93.94
O-Double classifier	99.12	91.21	93.22
Double classifier	99.46	93.33	95.88

Table 4. Experimental results of different networks on pan tilt video

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Table 4. Experimental results of different networks on pan tilt video

Network	Recognition accuracy /%		Speed /(frame·s^-1)		FLOPS/10⁶
Network	8-people	18-people	8-people	18-people	FLOPS/10⁶
ResNet-101^[13]	97.08	94.14	2.16	4.37	22.69×10³
ResNet-50^[13]	95.96	91.51	1.28	2.61	12.34×10³
DenseNet-201^[12]	96.78	94.98	1.16	2.36	8.5×10³
DenseNet-169^[12]	95.27	91.69	0.89	1.81	6.6×10³
ShuffleNet^[22]	92.05	87.53	0.12	0.26	591
MobileNet-v1^[20]	91.12	85.60	0.16	0.35	1.1×10³
MobileNet-v2^[21]	91.96	86.33	0.13	0.28	1.0×10³
MobileFaceNet^[11]	92.83	88.77	0.10	0.21	439.8
Dual-MobileFaceNet	96.24	94.68	0.14	0.29	1.0×10³

Table 5. Recognition accuracy of different algorithms%

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Table 5. Recognition accuracy of different algorithms%

Algorithm	8-people	18-people
DeepFace^[3]	87.53	83.67
Deep FR^[23]	88.54	84.27
DeepID2^[4]	88.94	84.25
FaceNet^[5]	89.35	85.33
SphereFace^[6]	90.58	87.68
CosFace^[7]	91.83	90.75
InsightFace^[8]	93.69	91.68
Double classifier	96.24	94.68

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Xichu Tian, Hansong Su, Gaohua Liu, Tengteng Liu. Improved Classroom Face Recognition Algorithm Based on InsightFace and Its Application[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221501

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Paper Information

Category: Machine Vision

Received: Feb. 17, 2020

Accepted: Mar. 25, 2020

Published Online: Nov. 5, 2020

The Author Email: Gaohua Liu (suppig@126.com)

DOI:10.3788/LOP57.221501

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology