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

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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Table 1. Comparison of MobileNetV2 and VGG16
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Table 1. Comparison of MobileNetV2 and VGG16
Network Params /MB FLOPs / $10^{9}$
VGG16 134.28 61.75
MobileNetV2 2.22 1.28

Table 2. Structural parameters of MobileNetV2 feature layer

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Table 2. Structural parameters of MobileNetV2 feature layer

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 3. Results of ablation experiments

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Table 3. Results of ablation experiments

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

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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	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 5. Comparison of CCR under inter-sensor detection

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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/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 6. Comparison of CCR under inter-database detection

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Table 6. Comparison of CCR under inter-database 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

Table 7. Comparison of calculated costs for each network
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Table 7. Comparison of calculated costs for each network
Network Params /MB FLOPs / $10^{9}$
RACNN 373.34 92.65
GHCLNet 23.51 4.12
DCLNet 6.96 2.88
RAINet 86.96 1.87

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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

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