Multi-challenge database for active liveness
Resumo
Facial authentication on mobile devices has been widely applied in various scenarios. The field of Face Liveness (or Face Anti-Spoofing, FAS) focuses on methods and tools for detecting attacks (spoofs) where a malicious user tries to impersonate someone else or obfuscate their own identity. The specific problem of Active Liveness consists of analyzing the input signal and also the user behavior while performing some required challenge to determine whether the presented face is real or not. Despite the large amount of public datasets in FAS, very few contemplate active liveness, a phenomenon that typically results in new solutions being developed and evaluated with in-house data that cannot be shared due to its sensitive nature. This disjoint character in evaluations leads to irreproducible works and greatly hinders the mutual contribution inside the scientific community. In this paper, we describe an approach for creating a new database for active liveness detection. In this database, volunteers use a mobile application (under alpha testing for Android and iOS devices) to record themselves executing three distinct interactions (challenges) with their heads, namely face close up, head orientation, and flashes. Users will be encouraged to acquire videos in different environments and times of day, which will contribute to high variance in the dataset. After all acquisitions, we will split the dataset into training and testing protocols.
Referências
D. Gollmann, “Computer security,” Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 5, pp. 544–554, 2010.
S. Z. S. Idrus, E. Cherrier, C. Rosenberger, and J.-J. Schwartzmann, “A review on authentication methods,” Australian Journal of Basic and Applied Sciences, vol. 7, no. 5, pp. 95–107, 2013.
L. Li, P. L. Correia, and A. Hadid, “Face recognition under spoofing attacks: countermeasures and research directions,” IET Biometrics, vol. 7, no. 1, pp. 3–14, 2018.
Z. Yu, Y. Qin, X. Li, C. Zhao, Z. Lei, and G. Zhao, “Deep learning for face anti-spoofing: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 5, pp. 5609–5631, 2022.
J. Yan, Z. Zhang, Z. Lei, D. Yi, and S. Z. Li, “Face liveness detection by exploring multiple scenic clues,” 2012 12th Int. Conf. on Control Automation Robotics & Vision (ICARCV), pp. 188–193, 2012.
M. Killioğlu, M. Tașkiran, and N. Kahraman, “Anti-spoofing in face recognition with liveness detection using pupil tracking,” in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2017, pp. 000 087–000 092.
M. Singh and A. Arora, “A robust anti-spoofing technique for face liveness detection with morphological operations,” Optik, vol. 139, pp. 347–354, 2017. [Online]. Available: [link].
M. Mohzary, K. J. Almalki, B.-Y. Choi, and S. Song, “Your eyes show what your eyes see (y-eyes): Challenge-response anti-spoofing method for mobile security using corneal specular reflections,” in 1st Workshop on Security and Privacy for Mobile AI, ser. MAISP’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 25–30. [Online]. Available: https://doi.org/10.1145/3469261.3469408
Y. Liu, Y. Tai, J. Li, S. Ding, C. Wang, F. Huang, D. Li, W. Qi, and R. Ji, “Aurora guard: Real-time face anti-spoofing via light reflection,” CoRR, vol. abs/1902.10311, 2019. [Online]. Available: [link]
J. M. D. Martino, Q. Qiu, T. Nagenalli, and G. Sapiro, “Liveness detection using implicit 3D features,” CoRR, vol. abs/1804.06702, 2018. [Online]. Available: [link]
W. Xu, J. Liu, S. Zhang, Y. Zheng, F. Lin, J. Han, F. Xiao, and K. Ren, “Rface: Anti-spoofing facial authentication using cots rfid,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
M. Ezz, M. Ayman, and A. Elshenawy Elsefy, “Challenge-response emotion authentication algorithm using modified horizontal deep learning,” Intelligent Automation and Soft Computing, vol. 35, pp. 3659–3675, 09 2022.
Z. Ming, J. Chazalon, M. M. Luqman, M. Visani, and J.-C. Burie, “Facelivenet: End-to-end networks combining face verification with interactive facial expression-based liveness detection,” in 2018 24th Int. Conf. on Pattern Recognition (ICPR). IEEE, 2018, pp. 3507–3512.
Y. Li, Z. Wang, Y. Li, R. Deng, B. Chen, W. Meng, and H. Li, “A closer look tells more: A facial distortion based liveness detection for face authentication,” in Asia CCS ’19: ACM Asia Conference on Computer and Communications Security, 07 2019, pp. 241–246.
X. Tan, Y. Li, J. Liu, and L. Jiang, “Face liveness detection from a single image with sparse low rank bilinear discriminative model,” in Computer Vision – ECCV 2010, K. Daniilidis, P. Maragos, and N. Paragios, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 504–517.
A. Anjos and S. Marcel, “Countermeasures to photo attacks in face recognition: A public database and a baseline,” in 2011 International Joint Conference on Biometrics (IJCB), 2011, pp. 1–7.
Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face antispoofing database with diverse attacks,” in 2012 5th IAPR Int. Conf. on Biometrics (ICB), 2012, pp. 26–31.
I. Chingovska, A. Anjos, and S. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” in 2012 BIOSIG - Int. Conf. of Biometrics Special Interest Group (BIOSIG), 2012, pp. 1–7.
D. Wen, H. Han, and A. K. Jain, “Face spoof detection with image distortion analysis,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 746–761, 2015.
K. Patel, H. Han, and A. K. Jain, “Secure face unlock: Spoof detection on smartphones,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 10, pp. 2268–2283, 2016.
A. Agarwal, D. Yadav, N. Kohli, R. Singh, M. Vatsa, and A. Noore, “Face presentation attack with latex masks in multispectral videos,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017.
Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, “OULUNPU: A mobile face presentation attack database with real-world variations,” in 2017 12th IEEE Int. Conf. on Automatic Face & Gesture Recognition (FG 2017), 2017, pp. 612–618.
Y. Liu, A. Jourabloo, and X. Liu, “Learning deep models for face anti-spoofing: Binary or auxiliary supervision,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep tree learning for zero-shot face anti-spoofing,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4675–4684.
Z. Mostaani, A. George, G. Heusch, D. Geissbühler, and S. Marcel, “The high-quality wide multi-channel attack (HQ-WMCA) database,” CoRR, vol. abs/2009.09703, 2020. [Online]. Available: [link]
Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, F. Zhou, and Z. Lei, “Deep spatial gradient and temporal depth learning for face anti-spoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5041–5050.
Y. Zhang, Z. Yin, Y. Li, G. Yin, J. Yan, J. Shao, and Z. Liu, “CelebAspoof: Large-scale face anti-spoofing dataset with rich annotations,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16. Springer, 2020, pp. 70–85.
D. Wang, J. Guo, Q. Shao, H. He, Z. Chen, C. Xiao, A. Liu, S. Escalera, H. J. Escalante, Z. Lei et al., “Wild face anti-spoofing challenge 2023: Benchmark and results,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 6379–6390.
Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face antispoofing using speeded-up robust features and fisher vector encoding,” IEEE Signal Processing Letters, vol. 24, no. 2, pp. 141–145, 2017.
T. Wang, J. Yang, Z. Lei, S. Liao, and S. Z. Li, “Face liveness detection using 3D structure recovered from a single camera,” in 2013 Int. Conf. on Biometrics (ICB), 2013, pp. 1–6.
W. Liu, X. Wei, T. Lei, X. Wang, H. Meng, and A. K. Nandi, “Data fusion based two-stage cascade framework for multi-modality face antispoofing,” IEEE Transactions on Cognitive and Developmental Systems, pp. 1–1, 2021.
S. Garg, S. Mittal, P. Kumar, and V. Anant Athavale, “DeBNet: Multilayer deep network for liveness detection in face recognition system,” in 2020 7th Int. Conf. on Signal Processing and Integrated Networks (SPIN), 2020, pp. 1136–1141.
X. Yang, W. Luo, L. Bao, Y. Gao, D. Gong, S. Zheng, Z. Li, and W. Liu, “Face anti-spoofing: Model matters, so does data,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3502–3511.
S. Luo, M. Kan, S. Wu, X. Chen, and S. Shan, “Face anti-spoofing with multi-scale information,” in 2018 24th Int. Conf. on Pattern Recognition (ICPR), 2018, pp. 3402–3407.
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face antispoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5294–5304.
Z. Yu, Y. Qin, H. Zhao, X. Li, and G. Zhao, “Dual-cross central difference network for face anti-spoofing,” CoRR, vol. abs/2105.01290, 2021. [Online]. Available: [link]
W. Zheng, M. Yue, S. Zhao, and S. Liu, “Attention-based spatialtemporal multi-scale network for face anti-spoofing,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 3, pp. 296–307, 2021.
Y. Wang, X. Song, T. Xu, Z. Feng, and X.-J. Wu, “From RGB to depth: Domain transfer network for face anti-spoofing,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 4280–4290, 2021.
K.-Y. Zhang, T. Yao, J. Zhang, Y. Tai, S. Ding, J. Li, F. Huang, H. Song, and L. Ma, “Face anti-spoofing via disentangled representation learning,” in European Conference on Computer Vision. Springer, 2020, pp. 641–657.
Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, F. Zhou, and Z. Lei, “Deep spatial gradient and temporal depth learning for face antispoofing,” 2020. [Online]. Available: [link]
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face anti-spoofing,” CoRR, vol. abs/2003.04092, 2020. [Online]. Available: [link]
Q. Zhou, K.-Y. Zhang, T. Yao, X. Lu, R. Yi, S. Ding, and L. Ma, “Instance-aware domain generalization for face anti-spoofing,” 2023.
Y. Sun, Y. Liu, X. Liu, Y. Li, and W.-S. Chu, “Rethinking domain generalization for face anti-spoofing: Separability and alignment,” 2023.
Z. Wang, Z. Wang, Z. Yu, W. Deng, J. Li, T. Gao, and Z. Wang, “Domain generalization via shuffled style assembly for face anti-spoofing,” 2022. [Online]. Available: [link]
M. Singh and A. S. Arora, “A novel face liveness detection algorithm with multiple liveness indicators,” Wireless Personal Communications, vol. 100, no. 4, pp. 1677–1687, Jun 2018. [Online]. Available: https://doi.org/10.1007/s11277-018-5661-1
J. Hernandez-Ortega, J. Fierrez, A. Morales, and D. Díaz, “A comparative evaluation of heart rate estimation methods using face videos,” 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1438–1443, 2020.
J. Hernandez-Ortega, R. Tolosana, J. Fierrez, and A. Morales, “Deepfakeson-phys: Deepfakes detection based on heart rate estimation,” ArXiv, vol. abs/2010.00400, 2020.
I. Sluganovic, M. Roeschlin, K. Rasmussen, and I. Martinovic, “Using reflexive eye movements for fast challenge-response authentication,” in CCS ’16: ACM SIGSAC Conference on Computer and Communications Security, 10 2016, pp. 1056–1067.
M. Shen, Z. Liao, L. Zhu, R. Mijumbi, X. Du, and J. Hu, “Iritrack: Liveness detection using irises tracking for preventing face spoofing attacks,” 2018. [Online]. Available: [link]
P. McShane and D. Stewart, “Challenge based visual speech recognition using deep learning,” in 2017 12th Int. Conf. for Internet Technology and Secured Transactions (ICITST), 2017, pp. 405–410.
E. Uzun, S. Chung, I. Essa, and W. Lee, “rtCaptcha: A real-time captcha based liveness detection system,” in Conference: The Network and Distributed System Security Symposium (NDSS), 02 2018.
C.-L. Chou, “Presentation attack detection based on score level fusion and challenge-response technique,” The Journal of Supercomputing, vol. 77, 05 2021.
M. De Marsico, M. Nappi, D. Riccio, and J.-L. Dugelay, “Moving face spoofing detection via 3D projective invariants,” in 2012 5th IAPR Int. Conf. on Biometrics (ICB). IEEE, 2012, pp. 73–78.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Large-scale celebfaces attributes (celeba) dataset,” Retrieved August, vol. 15, no. 2018, p. 11, 2018.
“Spark AR,” [link], accessed on: August 21, 2023.
“Reface,” [link], accessed on: August 21, 2023.
S. Z. S. Idrus, E. Cherrier, C. Rosenberger, and J.-J. Schwartzmann, “A review on authentication methods,” Australian Journal of Basic and Applied Sciences, vol. 7, no. 5, pp. 95–107, 2013.
L. Li, P. L. Correia, and A. Hadid, “Face recognition under spoofing attacks: countermeasures and research directions,” IET Biometrics, vol. 7, no. 1, pp. 3–14, 2018.
Z. Yu, Y. Qin, X. Li, C. Zhao, Z. Lei, and G. Zhao, “Deep learning for face anti-spoofing: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 5, pp. 5609–5631, 2022.
J. Yan, Z. Zhang, Z. Lei, D. Yi, and S. Z. Li, “Face liveness detection by exploring multiple scenic clues,” 2012 12th Int. Conf. on Control Automation Robotics & Vision (ICARCV), pp. 188–193, 2012.
M. Killioğlu, M. Tașkiran, and N. Kahraman, “Anti-spoofing in face recognition with liveness detection using pupil tracking,” in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2017, pp. 000 087–000 092.
M. Singh and A. Arora, “A robust anti-spoofing technique for face liveness detection with morphological operations,” Optik, vol. 139, pp. 347–354, 2017. [Online]. Available: [link].
M. Mohzary, K. J. Almalki, B.-Y. Choi, and S. Song, “Your eyes show what your eyes see (y-eyes): Challenge-response anti-spoofing method for mobile security using corneal specular reflections,” in 1st Workshop on Security and Privacy for Mobile AI, ser. MAISP’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 25–30. [Online]. Available: https://doi.org/10.1145/3469261.3469408
Y. Liu, Y. Tai, J. Li, S. Ding, C. Wang, F. Huang, D. Li, W. Qi, and R. Ji, “Aurora guard: Real-time face anti-spoofing via light reflection,” CoRR, vol. abs/1902.10311, 2019. [Online]. Available: [link]
J. M. D. Martino, Q. Qiu, T. Nagenalli, and G. Sapiro, “Liveness detection using implicit 3D features,” CoRR, vol. abs/1804.06702, 2018. [Online]. Available: [link]
W. Xu, J. Liu, S. Zhang, Y. Zheng, F. Lin, J. Han, F. Xiao, and K. Ren, “Rface: Anti-spoofing facial authentication using cots rfid,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
M. Ezz, M. Ayman, and A. Elshenawy Elsefy, “Challenge-response emotion authentication algorithm using modified horizontal deep learning,” Intelligent Automation and Soft Computing, vol. 35, pp. 3659–3675, 09 2022.
Z. Ming, J. Chazalon, M. M. Luqman, M. Visani, and J.-C. Burie, “Facelivenet: End-to-end networks combining face verification with interactive facial expression-based liveness detection,” in 2018 24th Int. Conf. on Pattern Recognition (ICPR). IEEE, 2018, pp. 3507–3512.
Y. Li, Z. Wang, Y. Li, R. Deng, B. Chen, W. Meng, and H. Li, “A closer look tells more: A facial distortion based liveness detection for face authentication,” in Asia CCS ’19: ACM Asia Conference on Computer and Communications Security, 07 2019, pp. 241–246.
X. Tan, Y. Li, J. Liu, and L. Jiang, “Face liveness detection from a single image with sparse low rank bilinear discriminative model,” in Computer Vision – ECCV 2010, K. Daniilidis, P. Maragos, and N. Paragios, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 504–517.
A. Anjos and S. Marcel, “Countermeasures to photo attacks in face recognition: A public database and a baseline,” in 2011 International Joint Conference on Biometrics (IJCB), 2011, pp. 1–7.
Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face antispoofing database with diverse attacks,” in 2012 5th IAPR Int. Conf. on Biometrics (ICB), 2012, pp. 26–31.
I. Chingovska, A. Anjos, and S. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” in 2012 BIOSIG - Int. Conf. of Biometrics Special Interest Group (BIOSIG), 2012, pp. 1–7.
D. Wen, H. Han, and A. K. Jain, “Face spoof detection with image distortion analysis,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 746–761, 2015.
K. Patel, H. Han, and A. K. Jain, “Secure face unlock: Spoof detection on smartphones,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 10, pp. 2268–2283, 2016.
A. Agarwal, D. Yadav, N. Kohli, R. Singh, M. Vatsa, and A. Noore, “Face presentation attack with latex masks in multispectral videos,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017.
Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, “OULUNPU: A mobile face presentation attack database with real-world variations,” in 2017 12th IEEE Int. Conf. on Automatic Face & Gesture Recognition (FG 2017), 2017, pp. 612–618.
Y. Liu, A. Jourabloo, and X. Liu, “Learning deep models for face anti-spoofing: Binary or auxiliary supervision,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep tree learning for zero-shot face anti-spoofing,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4675–4684.
Z. Mostaani, A. George, G. Heusch, D. Geissbühler, and S. Marcel, “The high-quality wide multi-channel attack (HQ-WMCA) database,” CoRR, vol. abs/2009.09703, 2020. [Online]. Available: [link]
Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, F. Zhou, and Z. Lei, “Deep spatial gradient and temporal depth learning for face anti-spoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5041–5050.
Y. Zhang, Z. Yin, Y. Li, G. Yin, J. Yan, J. Shao, and Z. Liu, “CelebAspoof: Large-scale face anti-spoofing dataset with rich annotations,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16. Springer, 2020, pp. 70–85.
D. Wang, J. Guo, Q. Shao, H. He, Z. Chen, C. Xiao, A. Liu, S. Escalera, H. J. Escalante, Z. Lei et al., “Wild face anti-spoofing challenge 2023: Benchmark and results,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 6379–6390.
Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face antispoofing using speeded-up robust features and fisher vector encoding,” IEEE Signal Processing Letters, vol. 24, no. 2, pp. 141–145, 2017.
T. Wang, J. Yang, Z. Lei, S. Liao, and S. Z. Li, “Face liveness detection using 3D structure recovered from a single camera,” in 2013 Int. Conf. on Biometrics (ICB), 2013, pp. 1–6.
W. Liu, X. Wei, T. Lei, X. Wang, H. Meng, and A. K. Nandi, “Data fusion based two-stage cascade framework for multi-modality face antispoofing,” IEEE Transactions on Cognitive and Developmental Systems, pp. 1–1, 2021.
S. Garg, S. Mittal, P. Kumar, and V. Anant Athavale, “DeBNet: Multilayer deep network for liveness detection in face recognition system,” in 2020 7th Int. Conf. on Signal Processing and Integrated Networks (SPIN), 2020, pp. 1136–1141.
X. Yang, W. Luo, L. Bao, Y. Gao, D. Gong, S. Zheng, Z. Li, and W. Liu, “Face anti-spoofing: Model matters, so does data,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3502–3511.
S. Luo, M. Kan, S. Wu, X. Chen, and S. Shan, “Face anti-spoofing with multi-scale information,” in 2018 24th Int. Conf. on Pattern Recognition (ICPR), 2018, pp. 3402–3407.
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face antispoofing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5294–5304.
Z. Yu, Y. Qin, H. Zhao, X. Li, and G. Zhao, “Dual-cross central difference network for face anti-spoofing,” CoRR, vol. abs/2105.01290, 2021. [Online]. Available: [link]
W. Zheng, M. Yue, S. Zhao, and S. Liu, “Attention-based spatialtemporal multi-scale network for face anti-spoofing,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 3, pp. 296–307, 2021.
Y. Wang, X. Song, T. Xu, Z. Feng, and X.-J. Wu, “From RGB to depth: Domain transfer network for face anti-spoofing,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 4280–4290, 2021.
K.-Y. Zhang, T. Yao, J. Zhang, Y. Tai, S. Ding, J. Li, F. Huang, H. Song, and L. Ma, “Face anti-spoofing via disentangled representation learning,” in European Conference on Computer Vision. Springer, 2020, pp. 641–657.
Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, F. Zhou, and Z. Lei, “Deep spatial gradient and temporal depth learning for face antispoofing,” 2020. [Online]. Available: [link]
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face anti-spoofing,” CoRR, vol. abs/2003.04092, 2020. [Online]. Available: [link]
Q. Zhou, K.-Y. Zhang, T. Yao, X. Lu, R. Yi, S. Ding, and L. Ma, “Instance-aware domain generalization for face anti-spoofing,” 2023.
Y. Sun, Y. Liu, X. Liu, Y. Li, and W.-S. Chu, “Rethinking domain generalization for face anti-spoofing: Separability and alignment,” 2023.
Z. Wang, Z. Wang, Z. Yu, W. Deng, J. Li, T. Gao, and Z. Wang, “Domain generalization via shuffled style assembly for face anti-spoofing,” 2022. [Online]. Available: [link]
M. Singh and A. S. Arora, “A novel face liveness detection algorithm with multiple liveness indicators,” Wireless Personal Communications, vol. 100, no. 4, pp. 1677–1687, Jun 2018. [Online]. Available: https://doi.org/10.1007/s11277-018-5661-1
J. Hernandez-Ortega, J. Fierrez, A. Morales, and D. Díaz, “A comparative evaluation of heart rate estimation methods using face videos,” 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1438–1443, 2020.
J. Hernandez-Ortega, R. Tolosana, J. Fierrez, and A. Morales, “Deepfakeson-phys: Deepfakes detection based on heart rate estimation,” ArXiv, vol. abs/2010.00400, 2020.
I. Sluganovic, M. Roeschlin, K. Rasmussen, and I. Martinovic, “Using reflexive eye movements for fast challenge-response authentication,” in CCS ’16: ACM SIGSAC Conference on Computer and Communications Security, 10 2016, pp. 1056–1067.
M. Shen, Z. Liao, L. Zhu, R. Mijumbi, X. Du, and J. Hu, “Iritrack: Liveness detection using irises tracking for preventing face spoofing attacks,” 2018. [Online]. Available: [link]
P. McShane and D. Stewart, “Challenge based visual speech recognition using deep learning,” in 2017 12th Int. Conf. for Internet Technology and Secured Transactions (ICITST), 2017, pp. 405–410.
E. Uzun, S. Chung, I. Essa, and W. Lee, “rtCaptcha: A real-time captcha based liveness detection system,” in Conference: The Network and Distributed System Security Symposium (NDSS), 02 2018.
C.-L. Chou, “Presentation attack detection based on score level fusion and challenge-response technique,” The Journal of Supercomputing, vol. 77, 05 2021.
M. De Marsico, M. Nappi, D. Riccio, and J.-L. Dugelay, “Moving face spoofing detection via 3D projective invariants,” in 2012 5th IAPR Int. Conf. on Biometrics (ICB). IEEE, 2012, pp. 73–78.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Large-scale celebfaces attributes (celeba) dataset,” Retrieved August, vol. 15, no. 2018, p. 11, 2018.
“Spark AR,” [link], accessed on: August 21, 2023.
“Reface,” [link], accessed on: August 21, 2023.
Publicado
06/11/2023
Como Citar
KAMAROWSKI, Bruno; ALMEIDA, Raul; BIESSECK, Bernardo; GRANADA, Roger; FÜHR, Gustavo; MENOTTI, David.
Multi-challenge database for active liveness. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 36. , 2023, Rio Grande/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 109-114.
DOI: https://doi.org/10.5753/sibgrapi.est.2023.27461.
