Anti-Spoofing Detection Method for Contact Lens Irises Based on Recurrent Attention Mechanism

Mengling Lu; Yuqing He; Junkai Yang; Weiqi Jin; Lijun Zhang

doi:10.3788/AOS202242.2315001

Journals >Acta Optica Sinica >Volume 42 >Issue 23 >Page 2315001 > Article

Acta Optica Sinica
Vol. 42, Issue 23, 2315001 (2022)

Anti-Spoofing Detection Method for Contact Lens Irises Based on Recurrent Attention Mechanism

Mengling Lu, Yuqing He^*, Junkai Yang, Weiqi Jin, and Lijun Zhang

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[in Chinese]

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DOI: 10.3788/AOS202242.2315001 Cite this Article Set citation alerts

Mengling Lu, Yuqing He, Junkai Yang, Weiqi Jin, Lijun Zhang. Anti-Spoofing Detection Method for Contact Lens Irises Based on Recurrent Attention Mechanism[J]. Acta Optica Sinica, 2022, 42(23): 2315001 Copy Citation Text

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Fig. 1. Real iris and textured contact lens iris. (a) Real iris; (b) textured contact lens iris

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Fig. 2. RAINet iris anti-spoofing detection network framework

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Fig. 3. Inverse residual block of Bottleneck

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Fig. 4. Location parameters of feature region. (a) Location parameters of iris region; (b) location parameters of texture region;(c) texture region after interpolation

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Fig. 5. Image masks of feature region. (a) Image masks of iris region; (b) image masks of texture region

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Fig. 6. Sample images from IIITD CLI database. (a) Real iris from Cogent; (b) textured contact lens iris from Cogent; (c) real iris from Vista; (d) texture contact lens iris from Vista

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Fig. 7. Sample images from ND series databases. (a) Real iris from NDC LG4000; (b) textured contact lens iris from NDC LG4000; (c) real iris from NDC AD100; (d) textured contact lens iris from NDC AD100; (e) real iris from NDCLD15; (f) textured contact lens iris from NDCLD15

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Fig. 8. ROC curves under intra-sensor detection

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Fig. 9. ROC curves under inter-sensor detection

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Fig. 10. ROC curves under inter-database detection

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Network	Params /MB	FLOPs / $10^{9}$
VGG16	134.28	61.75
MobileNetV2	2.22	1.28

Table 1. Comparison of MobileNetV2 and VGG16

Input	Operator	Factor	Output	Frequency	Step
224×224×3	Conv2d		32	1	2
112×112×32	Bottleneck	1	16	1	1
112×112×16	Bottleneck	6	24	2	2
56×56×24	Bottleneck	6	32	3	2
28×28×32	Bottleneck	6	64	4	2
14×14×64	Bottleneck	6	96	3	1
14×14×96	Bottleneck	6	160	1	1
7×7×160	Bottleneck	6	320	1	1

Table 2. Structural parameters of MobileNetV2 feature layer

Database	Network	$V_{C C R, c}$	$V_{C C R, i}$	$V_{C C R, a}$
Cogent （intra-sensor）	G-FCN	100.00	99.15	99.57
	I-FCN	100.00	99.43	99.71
	T-FCN	100.00	99.70	99.85
	RAINet3	100.00	99.43	99.71
	RAINet	100.00	99.70	99.85
Cogent/Vista （inter-sensor）	G-FCN	100.00	98.03	99.02
	I-FCN	100.00	100.00	100.00
	T-FCN	100.00	99.48	99.74
	RAINet3	100.00	99.34	99.67
	RAINet	100.00	99.68	99.84
Cogent/NDC LG4000 （inter-database）	G-FCN	91.25	100.00	95.62
	I-FCN	96.22	100.00	98.11
	T-FCN	96.45	100.00	98.22
	RAINet3	94.56	100.00	97.27
	RAINet	96.93	100.00	98.46

Table 3. Results of ablation experiments

Database	Network	$V_{C C R, c}$	$V_{C C R, i}$	$V_{C C R, a}$
Cogent	RACNN	100.00	99.24	99.62
	GHCLNet	100.00	89.86	94.98
	DCLNet	99.10	94.19	96.64
	RAINet	100.00	99.70	99.85
Vista	RACNN	100.00	97.72	98.86
	GHCLNet	100.00	94.60	97.30
	DCLNet	100.00	93.19	96.60
	RAINet	100.00	100.00	100.00
NDC LG4000	RACNN	100.00	99.21	99.60
	GHCLNet	99.75	95.24	97.50
	DCLNet	99.93	92.86	96.40
	RAINet	100.00	99.78	99.89
NDC AD100	RACNN	100.00	99.52	99.76
	GHCLNet	100.00	91.67	95.84
	DCLNet	98.50	89.49	94.00
	RAINet	100.00	100.00	100.00

Table 4. Comparison of CCR under intra-sensor detection unit: %

Database	Network	$V_{C C R, c}$	$V_{C C R, i}$	$V_{C C R, a}$
Cogent/Vista	RACNN	100.00	99.68	99.84
	GHCLNet	99.25	93.40	96.33
	DCLNet	99.83	89.55	94.69
	RAINet	100.00	99.68	99.84
Vista/Cogent	RACNN	90.21	96.17	93.19
	GHCLNet	85.36	96.74	91.05
	DCLNet	99.82	81.43	90.63
	RAINet	94.54	97.48	96.03
NDC LG4000/AD100	RACNN	100.00	97.33	98.66
	GHCLNet	98.00	91.90	94.95
	DCLNet	100.00	92.00	96.00
	RAINet	100.00	100.00	100.00
NDC AD100/LG4000	RACNN	100.00	84.18	92.09
	GHCLNet	100.00	81.25	90.63
	DCLNet	97.92	83.00	90.46
	RAINet	100.00	86.76	93.36

Table 5. Comparison of CCR under inter-sensor detection

Database	Network	$V_{C C R, c}$	$V_{C C R, i}$	$V_{C C R, a}$
Cogent/NDC LG4000	RACNN	93.12	100.00	96.56
	GHCLNet	90.07	100.00	95.02
	DCLNet	87.94	100.00	93.95
	RAINet	96.93	100.00	98.46
Cogent/ND 15	RACNN	100.00	88.80	94.40
	GHCLNet	90.07	100.00	95.02
	DCLNet	100.00	81.40	90.70
	RAINet	100.00	92.20	96.10
Cogent/ND 19	RACNN	92.70	99.80	96.15
	GHCLNet	90.17	99.76	93.46
	DCLNet	88.68	100.00	92.57
	RAINet	93.90	100.00	96.95
NDC LG4000/ND 15	RACNN	98.00	98.80	98.40
	GHCLNet	99.60	95.60	97.60
	DCLNet	98.80	99.40	99.10
	RAINet	99.40	99.20	99.30
NDC LG4000/ND 19	RACNNt	100.00	99.52	99.77
	GHCLNet	100.00	94.05	97.96
	DCLNet	100.00	89.76	96.49
	RAINet	100.00	99.76	99.91
ND 15/ND 19	RACNN	92.82	100.00	96.43
	GHCLNet	93.03	100.00	95.42
	DCLNet	92.41	100.00	95.02
	RAINet	92.91	100.00	96.45