Face Liveness Detection Algorithm Based on Real Face Category Adversarial Mechanism

Lei Zhang; Shaoyan Gai; Feipeng Da

doi:10.3788/LOP220649

Laser & Optoelectronics Progress, Volume. 60, Issue 10, 1010007(2023)

Face Liveness Detection Algorithm Based on Real Face Category Adversarial Mechanism

Lei Zhang^1,2, Shaoyan Gai^1,2、*, and Feipeng Da^1,2,3

Author Affiliations

¹School of Automation, Southeast University, Nanjing 210096, Jiangsu , China

²Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, Jiangsu , China

³Shenzhen Research Institute, Southeast University, Shenzhen 518063, Guangdong , China

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Abstract Get PDF(in Chinese)

Figures & Tables(12)

Fig. 1. Motivation of different methods

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Fig. 2. Overall architecture of the proposed model

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Fig. 3. Multi-scale attention fusion module

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Fig. 4. Illustration of triplet loss

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Fig. 5. t-SNE visualization for the classification features obtained by baseline method and proposed method under the I&C&M to O testing task

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Fig. 6. Grad-CAM visualization under the I&C&M to O testing task

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Table 1. Details of four datasets
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Table 1. Details of four datasets
Dataset Number of identities Number of videos Number of real categories Number of fake categories
MSU-MFSD 35 280 2 6
CASIA-FASD 50 600 3 9
Idiap Replay-Attack 50 1200 4 20
OULU-NPU 55 4950 18 72

Table 2. Evaluation results of different components of the proposed method

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Table 2. Evaluation results of different components of the proposed method

Method	O&C&I to M		O&M&I to C		O&C&M to I		I&C&M to O
Method	HTER /%	AUC /%	HTER /%	AUC /%	HTER /%	AUC /%	HTER /%	AUC /%
w /o att	5.27	95.20	12.11	94.31	10.07	95.99	13.54	93.22
w /o ad	5.71	96.10	10.77	94.51	14.92	93.05	12.30	94.97
w /o tri	7.14	96.53	10.66	95.19	21.42	80.51	22.06	86.37
all	4.52	97.24	9.88	95.46	9.21	96.97	11.45	95.32

Table 3. Comparison results between the proposed method and the corresponding baseline method
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Table 3. Comparison results between the proposed method and the corresponding baseline method
Method O&C&I to M O&M&I to C O&C&M to I I&C&M to O
HTER /% AUC /% HTER /% AUC /% HTER /% AUC /% HTER /% AUC /%
SSDG 7.38 97.17 10.44 95.94 11.71 96.59 15.61 91.54
Proposed method 4.52 97.24 9.88 95.46 9.21 96.97 11.45 95.32

Table 4. Comparison result of different domain generalization methods in the case of limited source domains

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Table 4. Comparison result of different domain generalization methods in the case of limited source domains

Method	M&I to C		M&I to O
Method	HTER /%	AUC /%	HTER /%	AUC /%
MS-LBP^［23］	51.16	52.09	43.63	58.07
IDA^［4］	45.16	58.80	54.52	42.17
LBP-TOP^［24］	45.27	54.88	47.26	50.21
MADDG^［10］	41.02	64.33	39.35	65.10
SSDG-M^［9］	31.89	71.29	36.01	66.88
DRDG^［11］	31.28	71.50	33.35	69.14
DASN^［12］	21.48	83.41	21.74	80.87
Proposed method	17.88	89.91	22.70	86.17

Table 5. Comparison result of different backbone networks
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Table 5. Comparison result of different backbone networks
Backbone FLOPs /10⁹ Params /10⁶ Speed /（frame·s^-1） Avg HTER /% Avg AUC /%
Resnet50 5.37 25.60 46.57 12.05 93.51
Resnet34 4.79 21.81 52.65 12.31 93.87
Resnet18 2.37 11.70 100.75 8.76 96.29

Table 6. Comparison result between the proposed method and other methods for domain generalization on face anti-spoofing

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Table 6. Comparison result between the proposed method and other methods for domain generalization on face anti-spoofing

Method	O&C&I to M		O&M&I to C		O&C&M to I		I&C&M to O
Method	HTER /%	AUC /%	HTER /%	AUC /%	HTER /%	AUC /%	HTER /%	AUC /%
MS-LBP^［23］	29.76	78.50	54.28	44.98	50.30	51.64	50.29	49.31
Binary CNN^［25］	29.25	82.87	34.88	71.94	34.47	65.88	29.61	77.54
IDA^［4］	66.67	27.86	55.17	39.05	28.35	78.25	54.20	44.59
Color Texture^［26］	28.09	78.47	30.58	76.89	40.40	62.78	63.59	32.71
LBP-TOP^［24］	36.90	70.80	33.52	73.15	29.14	71.69	30.17	77.61
Auxiliary（Depth）	22.72	85.88	33.52	73.15	29.14	71.69	30.17	77.61
Auxiliary^［27］	—	—	28.40	—	27.60	—	—	—
MADDG^［10］	17.69	88.06	24.50	84.51	22.19	84.99	27.89	80.02
SSDG^［9］	7.38	97.17	10.44	95.94	11.71	96.59	15.61	91.54
DRDG^［11］	12.43	95.81	19.05	88.79	15.56	91.79	15.63	91.75
DASN^［12］	8.33	96.31	12.04	95.33	13.38	86.63	11.77	94.65
Proposed method	4.52	97.24	9.88	95.46	9.21	96.97	11.45	95.32

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Lei Zhang, Shaoyan Gai, Feipeng Da. Face Liveness Detection Algorithm Based on Real Face Category Adversarial Mechanism[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010007

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Paper Information

Category: Image Processing

Received: Jan. 27, 2022

Accepted: Feb. 16, 2022

Published Online: May. 10, 2023

The Author Email: Gai Shaoyan (qxxymm@163.com)

DOI:10.3788/LOP220649

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Details of four datasets

Table 1. Details of four datasets

Table 2. Evaluation results of different components of the proposed method

Table 2. Evaluation results of different components of the proposed method

Table 3. Comparison results between the proposed method and the corresponding baseline method

Table 3. Comparison results between the proposed method and the corresponding baseline method

Table 4. Comparison result of different domain generalization methods in the case of limited source domains

Table 4. Comparison result of different domain generalization methods in the case of limited source domains

Table 5. Comparison result of different backbone networks

Table 5. Comparison result of different backbone networks

Table 6. Comparison result between the proposed method and other methods for domain generalization on face anti-spoofing

Table 6. Comparison result between the proposed method and other methods for domain generalization on face anti-spoofing