Face Recognition Based on Lightweight Recursive Residual Neural Network

Xiuling Zhang; Kaixuan Zhou; Qijun Wei; Jinxiang Li

doi:10.3788/LOP57.221506

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 22 >Page 221506 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 22, 221506 (2020)

Face Recognition Based on Lightweight Recursive Residual Neural Network

Xiuling Zhang^1、2、*, Kaixuan Zhou¹, Qijun Wei¹, and Jinxiang Li¹

Author Affiliations

¹Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 0 66004, China

²National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao, Hebei 0 66004, China

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DOI: 10.3788/LOP57.221506 Cite this Article Set citation alerts

Xiuling Zhang, Kaixuan Zhou, Qijun Wei, Jinxiang Li. Face Recognition Based on Lightweight Recursive Residual Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221506 Copy Citation Text

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Fig. 1. Residual model 1

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Fig. 2. Residual model 2

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Fig. 3. Gradient weighted global average pooling process

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Fig. 4. Schematic of pretreatment process

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Fig. 5. Some test data pairs. (a) Same face images; (b) different face images

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Fig. 6. Experimental training curve of M4 model

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Fig. 7. Test curves of M4 model

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Input	Operator	E	C	n	s
112×112×3	Conv 3×3	-	64	1	2
56×56×64	DW Conv 3×3	-	64	1	1
56×56×64	Bottleneck	3/2	64	5	2
28×28×64	Bottleneck	3	128	1	2
14×14×128	Bottleneck	3/2	128	6	1
14×14×128	Bottleneck	3	128	1	2
7×7×128	Bottleneck	3/2	128	2	1
7×7×128	Conv 1×1	-	512	1	1
7×7×512	Linear WGAP	-	512	1	1
1×1×512	Linear Conv 1×1	-	128	1	1

Table 1. Network configuration details

Network	LFW/%	AgeDB/%	CFP-FP/%	FLOP	Speed/ms	Model size/MB	Parameter number /10⁶
ShiftFaceNet^[16]	96.00	-	-	-	-	3.1	0.780
Light CNN-29^[17]	99.33	-	-	-	-	50.0	4.000
LMobileNetE^[13]	99.25	95.12	90.68	3.17×10⁸	24	112.0	3.200
MobileFacenet^[9]	99.29	95.58	91.17	4.33×10⁸	39	4.0	0.990
M1	99.37	95.98	91.60	3.84×10⁸	34	3.9	0.818
M2	99.20	94.68	90.42	3.77×10⁸	31	3.9	0.795
M4	99.37	96.08	92.02	3.84×10⁸	34	3.9	0.816
M5	99.22	94.72	90.53	3.77×10⁸	31	3.9	0.793

Table 2. Different model accuracy and parameter ratio

Network	LFW	AgeDB	CFP-FP
SE-LResNet18^[13]	99.53	96.18	93.42
SE-LM6-18	99.62	96.95	94.34
SE-LM7-18	99.67	96.92	94.23

Table 3. Comparison of model recognition accuracy unit: %

Xiuling Zhang, Kaixuan Zhou, Qijun Wei, Jinxiang Li. Face Recognition Based on Lightweight Recursive Residual Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221506

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