Active Face Spoof Detection Using Image Distortion Analysis

With the rising use of facial recognition systems in a range of real-world scenarios and applications, attackers are also increasing their efforts, with a number of spoofing techniques emerging. As a result, developing a reliable spoof detection mechanism is critical. Active-based techniques have been shown to be good at finding spoofs, but they have a number of problems, such as being intrusive, expensive, hard to compute, not being able to be used in many situations, and usually needing extra hardware. This research presented an active-based robust spoof detection technique capable of detecting a wide range of media or 2D attacks while being less intrusive, less expensive, low in complexity, and more generalizable than other active-based techniques. It doesn't require any additional hardware, so it can easily be integrated into current systems. The distortion variations of video frames of the user's face collected at varying distances from the camera are analyzed to detect spoofing. Both the legitimate and spoof attack datasets were created using real-world facial photo and video data. The proposed approach achieved a spoof detection accuracy of 98.18% using both machine learning classifiers and a deep learning model, with an equal error rate and a half total error rate as low as 0.023 and 0.021, respectively.

Keywords:

Face Anti-spoofing, Face Spoof Detection, Face Recognition Biometrics, Image Distortion Analysis.,

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A. Singh, P. Joshi, G.C. Nandi, Face recognition with liveness detection using eye and mouth movement, 2014 Int. Conf. Signal Propag. Comput. Technol. (ICSPCT 2014). (2014) 592–597.
D. Wen, H. Han, A.K. Jain, Face Spoof Detection With Image Distortion Analysis, IEEE Trans. Inf. Forensics Secur. 10 (2015) 746–761. https://doi.org/10.1109/TIFS.2015.2400395.
L. Sun, G. Pan, Z. Wu, S. Lao, Blinking-Based Live Face Detection Using Conditional Random Fields, in: S.-W. Lee, S.Z. Li (Eds.), Adv. Biometrics, Springer Berlin Heidelberg, Berlin, Heidelberg, 2007: pp. 252–260.
C.N. Karson, Spontaneous eye-blink rates and dopaminergic systems., Brain. 106 (Pt 3) (1983) 643–653. https://wwww.unboundmedicine.com/medline/citation/6640274/Spontaneous_eye_blink_rates_and_dopaminergic_systems_.
S. Kumar, S. Singh, J. Kumar, A comparative study on face spoofing attacks, in: 2017 Int. Conf. Comput. Commun. Autom., 2017: pp. 1104–1108. https://doi.org/10.1109/CCAA.2017.8229961.
J. Galbally, S. Marcel, J. Fierrez, Biometric Antispoofing Methods: A Survey in Face Recognition, IEEE Access. 2 (2014) 1530–1552. https://doi.org/10.1109/ACCESS.2014.2381273.
D. Menotti, G. Chiachia, A. Pinto, W.R. Schwartz, H. Pedrini, A.X. Falcão, A. Rocha, Deep Representations for Iris, Face, and Fingerprint Spoofing Detection, IEEE Trans. Inf. Forensics Secur. 10 (2015) 864–879. https://doi.org/10.1109/TIFS.2015.2398817.
A. Pinto, W. Schwartz, H. Pedrini, A. Rocha, Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks, Inf. Forensics Secur. IEEE Trans. 10 (2015) 1025–1038. https://doi.org/10.1109/TIFS.2015.2395139.
J. Yang, Z. Lei, D. Yi, S.Z. Li, Person-Specific Face Antispoofing With Subject Domain Adaptation, IEEE Trans. Inf. Forensics Secur. 10 (2015) 797–809. https://doi.org/10.1109/TIFS.2015.2403306.
I. Pavlidis, P. Symosek, The imaging issue in an automatic face/disguise detection system, in: Proc. IEEE Work. Comput. Vis. Beyond Visible Spectr. Methods Appl. (Cat. No.PR00640), 2000: pp. 15–24. https://doi.org/10.1109/CVBVS.2000.855246.
R. TABULA, “Trusted biometrics under spoofing attacks,” Http://Www.Tabularasa-Euproject.Org/. (n.d.). I. Chingovska, A. Anjos, S. Marcel, On the effectiveness of local binary patterns in face anti-spoofing, 2012 BIOSIG - Proc. Int. Conf. Biometrics Spec. Interes. Gr. (2012) 1–7.
Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, S. Li, A face antispoofing database with diverse attacks, 2012 5th IAPR Int. Conf. Biometrics. (2012) 26–31.
X. Tan, Y. Li, J. Liu, L. Jiang, Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model, in: K. Daniilidis, P. Maragos, N. Paragios (Eds.), Comput. Vis. -- ECCV 2010, Springer Berlin Heidelberg, Berlin, Heidelberg, 2010: pp. 504–517.
N. Erdogmus, S. Marcel, Spoofing in 2D Face Recognition with 3D Masks and Anti-spoofing with Kinect, in: Biometrics Theory, Appl. Syst., 2013.
M. Killioğlu, M. Taşkiran, N. Kahraman, Anti-spoofing in face recognition with liveness detection using pupil tracking, in: 2017 IEEE 15th Int. Symp. Appl. Mach. Intell. Informatics, 2017: pp. 87–92. https://doi.org/10.1109/SAMI.2017.7880281.
T. Dhawanpatil, B. Joglekar, A Review Spoof Face Recognition Using LBP Descriptor, in: A.K. Somani, S. Srivastava, A. Mundra, S. Rawat (Eds.), Proc. First Int. Conf. Smart Syst. Innov. Comput., Springer Singapore, Singapore, 2018: pp. 661–668. https://doi.org/https://doi.org/10.1007/978-981-10-5828-8_63.
G. Albakri, S. Alghowinem, The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras@, Sensors (Basel). 19 (2019).
Y. Ma, L. Wu, Z. Li, F. liu, A novel face presentation attack detection scheme based on multi-regional convolutional neural networks, Pattern Recognit. Lett. 131 (2020) 261–267.
L. Feng, L.-M. Po, Y. Li, X. Xu, F. Yuan, T.C.-H. Cheung, K.-W. Cheung, Integration of image quality and motion cues for face anti-spoofing: A neural network approach, J. Vis. Commun. Image Represent. 38 (2016) 451–460. https://doi.org/https://doi.org/10.1016/j.jvcir.2016.03.019.
P. Wild, P. Radu, L. Chen, J. Ferryman, Robust multimodal face and fingerprint fusion in the presence of spoofing attacks, Pattern Recognit. 50 (2016) 17–25. https://doi.org/https://doi.org/10.1016/j.patcog.2015.08.007.
B. Geng, C. Lang, J. Xing, S. Feng, W. Jun, MFAD: A Multi-modality Face Anti-spoofing Dataset, in: 2019: pp. 214–225. https://doi.org/10.1007/978-3-030-29911-8_17.
T. Chugh, A.K. Jain, Fingerprint Spoof Detection: Temporal Analysis of Image Sequence, CoRR. abs/1912.0 (2019). http://arxiv.org/abs/1912.08240.
V. Holub, J. Fridrich, Digital Image Steganography Using Universal Distortion, in: IH MMSec 2013 - Proc. 2013 ACM Inf. Hiding Multimed. Secur. Work., 2013. https://doi.org/10.1145/2482513.2482514.
J. Cheng, A.C. Kot, S. Rahardja, Steganalysis of Binary Cartoon Image using Distortion Measure, in: 2007 IEEE Int. Conf. Acoust. Speech Signal Process. - ICASSP ’07, 2007: pp. II-261-II–264. https://doi.org/10.1109/ICASSP.2007.366222.
P.P.D. Raval, R.R. Sedamkar, S. Kulkarni, Face Spoofing Detection Using Image Distortion Features, in: 2017.
M. Bryson, M. Johnson-Roberson, O. Pizarro, S. Williams, Colour-Consistent Structure-from-Motion Models using Underwater Imagery, in: 2012. https://doi.org/10.15607/RSS.2012.VIII.005.
X. Sun, L. Huang, C. Liu, Context based face spoofing detection using active near-infrared images, in: 2016: pp. 4262–4267. https://doi.org/10.1109/ICPR.2016.7900303.
Mohamed, Shaimaa, Ghoneim, Amr, Youssif, Aliaa, Visible/Infrared face spoofing detection using texture descriptors, MATEC Web Conf. 292 (2019) 4006. https://doi.org/10.1051/matecconf/201929204006.
B.R. Naidu, P.V.G.D. Reddy, Fusion of face and voice for a multimodal biometric recognition system, Int. J. Eng. Adv. Technol. 8 (2019) 506–515.
Y. Li, Z. Wang, Y. Li, R. Deng, B. Chen, W. Meng, H. Li, A Closer Look Tells More: A Facial Distortion Based Liveness Detection for Face Authentication, in: Proc. 2019 ACM Asia Conf. Comput. Commun. Secur., Association for Computing Machinery, New York, NY, USA, 2019: pp. 241–246. https://doi.org/10.1145/3321705.3329850.
Dlib Python API Tutorials [Electronic resource] – Access mode: http://dlib.net/python/index.html, (n.d.). http://dlib.net/python/index.html.
D.E. King, Dlib-ml: A Machine Learning Toolkit, J. Mach. Learn. Res. 10 (2009) 1755–1758.
E. Osuna, R. Freund, F. Girosit, Training support vector machines: an application to face detection, in: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 1997: pp. 130–136.
H. Yu, J. Yang, A direct LDA algorithm for high-dimensional data—with application to face recognition, Pattern Recognit. 34 (2001) 2067–2070.
S. Bharadwaj, T.I. Dhamecha, M. Vatsa, R. Singh, Computationally efficient face spoofing detection with motion magnification, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Work., 2013: pp. 105–110.
C. Priyanka, Sharma; Neha, Spoofing Face Detection using LBP Descriptor and KNN Classifier in Image Processing, Int. J. Recent Technol. Eng. Volume-8 (n.d.).
K. Samrity, Saini; Kiranpreet, KNN Classification for the Face Spoof Detection, Int. J. Sci. Eng. Res. Volume 10 (n.d.) 1101–1106.
Y. Du, T. Qiao, M. Xu, N. Zheng, Towards Face Presentation Attack Detection Based on Residual Color Texture Representation, Secur. Commun. Networks. 2021 (2021) 6652727. https://doi.org/10.1155/2021/6652727.
Kanika kalihal ; Jaspreet Kaur, A Review on Different Face Spoof Detection Techniques in Biometric Systems, Int. J. Sci. Res. Eng. Trends. Volume 5 (n.d.).
RecogTech, FAR and FRR: security level versus user convenience, Https://Www.Recogtech.Com/En/Knowledge-Base/Security-Level-versus-User-Convenience, (Retrieved on 31st/01/2022). (n.d.) (Retrieved on 31st/01/2022).
S. Bengio, J. Mariéthoz, A Statistical Significance Test for Person Authentication, Speak. Lang. Recognit. Work. (2004).
H. Raeisi Shahraki, S. Pourahmad, N. Zare, K Important Neighbors: A Novel Approach to Binary Classification in High Dimensional Data, Biomed Res. Int. 2017 (2017) 7560807. https://doi.org/10.1155/2017/7560807.