Abstract
Face anti-spoofing has been attracting attention because of its prominent role in the security of face recognition systems. A face spoofing attack is launched on the authentication system by the attacker using the picture or video of the genuine user to get access to the services or information. This paper proposes local binary pattern (LBP) and convolutional neural network (CNN) based feature fusion model for classifying spoof face images. Initially, the images are pre-processed to remove the effect of the background in the feature extraction. The Viola-Jones algorithm is used for detecting and cropping the faces from the images. The color features of the pre-processed RGB images and LBP features are provided to the CNN. This feature combination reduces the requirement of a CNN-based deep learning architecture for providing a good classification performance. To validate the proposed model, it is trained and tested on NUAA and MSU-MFSD datasets. The experiment recorded the ACER of 0.21 and 0.20 on the MSU-MFSD and NUAA datasets, respectively.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability:
The datasets analyzed during the current study are publicly available. These datasets were derived from the following public domain resources: http://parnec.nuaa.edu.cn/_upload/tpl/02/db/731/template731/pages/xtan/NUAAImposterDB_download.html, https://sites.google.com/site/huhanhomepage/download.
References
Salem E, Hassaballah M, Mahmoud MM, Ali AMM (2021) Facial features detection: a comparative study. In: The international conference on artificial intelligence and computer vision, pp. 402–412, Springer, Cham
Hassaballah M, Aly S (2015) Face recognition: challenges, achievements and future directions. IET Comput Vis 9(4):614–626
Patel K, Han H, Jain AK (2016) Secure face unlock: spoof detection on smartphones. IEEE Trans Inf Forensics Secur 11(10):2268–2283
Chingovska I, Anjos A, Marcel S (2012) September. On the effectiveness of local binary patterns in face anti-spoofing. In: 2012 BIOSIG-proceedings of the international conference of biometrics special interest group (BIOSIG), pp 1–7. IEEE
Boulkenafet Z, Komulainen J, Hadid A (2016) Face antispoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Process Lett 24(2):141–145
Peixoto B, Michelassi C, Rocha A (2011) September. Face liveness detection under bad illumination conditions. In: 2011 18th IEEE international conference on image processing, pp 3557–3560. IEEE
Chai D, Bouzerdoum A (2000) A Bayesian approach to skin color classification in YCbCr color space. In: 2000 TENCON proceedings. Intelligent systems and technologies for the new millennium (Cat. No. 00CH37119), vol 2, pp 421–424. IEEE
Smith JW, Torres JE, Holmquist ND (1981) Herpes simplex virus type 2 and human cervical cancer: Relationship between cellular and humoral immune assays for the detection of previous infection. JNCI J Natl Cancer Inst 66(6):1031–1036
Freitas Pereira TD, Anjos A, Martino JMD, Marcel S (2012) LBP-TOP based countermeasure against face spoofing attacks. In: Asian conference on computer vision, pp. 121–132. Springer, Berlin
De Souza GB, da Silva Santos DF, Pires RG, Marana AN, Papa JP (2017) Deep texture features for robust face spoofing detection. IEEE Trans Circuits Syst II Express Briefs 64(12):1397–1401
Komulainen J, Hadid A, Pietikäinen M (2013) Context based face anti-spoofing. In: 2013 IEEE sixth international conference on biometrics: theory, applications and systems (BTAS), pp 1–8. IEEE
Karanwal S, Diwakar M (2021) Neighborhood and center difference-based-LBP for face recognition. Pattern Anal Appl 24(2):741–761
Zhang S, Liu A, Wan J, Liang Y, Guo G, Escalera S, Li SZ (2020) Casia-surf: a large-scale multi-modal benchmark for face anti-spoofing. IEEE Trans Biometrics Behav Identity Sci 2(2):182–193
Parkin A, Grinchuk O (2019) Recognizing multi-modal face spoofing with face recognition networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops
Zhang Z, Yan J, Liu S, Lei Z, Yi D, Li SZ (2012) March. A face antispoofing database with diverse attacks. In: 2012 5th IAPR international conference on biometrics (ICB), pp 26–31. IEEE
Shen T, Huang Y, Tong Z (2019) Facebagnet: Bag-of-local-features model for multi-modal face anti-spoofing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops
Sun W, Song Y, Zhao H, Jin Z (2020) A face spoofing detection method based on domain adaptation and lossless size adaptation. IEEE Access 8:66553–66563
Boulkenafet Z, Komulainen J, Li L, Feng X, Hadid A (2017) May. OULU-NPU: a mobile face presentation attack database with real-world variations. In: 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017), pp 612–618. IEEE
Li H, He P, Wang S, Rocha A, Jiang X, Kot AC (2018) Learning generalized deep feature representation for face anti-spoofing. IEEE Trans Inf Forensics Secur 13(10):2639–2652
Zhou F, Gao C, Chen F, Li C, Li X, Yang F, Zhao Y (2019) July. Face anti-spoofing based on multi-layer domain adaptation. In: 2019 IEEE international conference on multimedia & expo workshops (ICMEW), pp 192–197 IEEE
Katika BR, Karthik K (2020) Face anti-spoofing by identity masking using random walk patterns and outlier detection. Pattern Anal Appl 23(4):1735–1754
Vareto RH, Schwartz WR (2021) Face spoofing detection via ensemble of classifiers toward low-power devices. Pattern Anal Appl 24(2):511–521
Tu X, Ma Z, Zhao J, Du G, Xie M, Feng J (2020) Learning generalizable and identity-discriminative representations for face anti-spoofing. ACM Trans Intell Syst Technol (TIST) 11(5):1–19
Arashloo SR, Kittler J, Christmas W (2015) Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features. IEEE Trans Inf Forensics Secur 10(11):2396–2407
Guo M, Wang Z, Zhuang S (2021) Feature extraction of dual-convolutional network with LBP for face anti-spoofing. In: 2021 international conference on computer engineering and artificial intelligence (ICCEAI), pp. 305–309. IEEE
Wu H, Zeng D, Hu Y, Shi H, Mei T (2022) Dual spoof disentanglement generation for face anti-spoofing with depth uncertainty learning. IEEE Trans Circuits Syst Video Technol 32(7):4626–4638. https://doi.org/10.1109/TCSVT.2021.3133620
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Singh, R.P., Dash, R. & Mohapatra, R.K. LBP and CNN feature fusion for face anti-spoofing. Pattern Anal Applic 26, 773–782 (2023). https://doi.org/10.1007/s10044-023-01132-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-023-01132-4