Abstract
Biometric authentication is becoming more prevalent for secured authentication systems. However, the biometric systems can be deceived by the imposters in several ways. Among other imposter attacks, print attacks, mask-attacks, and replay-attacks fall under the presentation attack category. The biometric images, especially iris and face, are vulnerable to different presentation attacks. This research applies deep learning approaches to mitigate the presentation attacks in a biometric access control system. Our contribution in this paper is two-fold: first, we applied the wavelet transform to extract the features from the biometric images. Second, we modified the deep residual neural net and applied it on the spoof datasets in an attempt to detect the presentation attacks. This research applied deep learning technique on three biometric spoof datasets: ATVS, CASIA two class, and CASIA cropped image sets. The datasets used in this research contain images that are captured both in a controlled and uncontrolled environment along with different resolution and sizes. We obtained the best accuracy of 93% on the ATVS Iris dataset. For CASIA two class and CASIA cropped datasets, we achieved test accuracies of 91% and 82%, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fierrez, J., Ortega-Garcia, J., Torre-Toledano, D., Gonzalez-Rodriguez, J.: BioSec baseline corpus: a multimodal biometric database. Pattern Recognit. 40(4), 1389–1392 (2007)
Galbally, J., Ortiz-Lopez, J., Fierrez, J., Ortega-Garcia, J.: Iris liveness detection based on quality related features. In Proceedings of the International Conference on Biometrics, New Delhi, India, ICB, pp. 271–276, March 2012
Yang, J., Lei, Z., Li, S.: Learn convolutional neural network for face anti-spoofing. arXiv:1408.5601v2 [cs.CV], August 2014
Menotti, D., et al.: Deep representations for iris, face, and fingerprint spoofing detection, arXiv:1410.1980v3 [cs.CV], Pre-print of article that will appear in IEEE Transactions on Information Forensics and Security (T.IFS), 29 January 2015
Manjani, I., Tariyal, S., Vatsa, M., Singh, R., Majumdar, A.: Detecting silicone mask-based presentation attack via deep dictionary learning. IEEE Trans. Inf. Forensics Secur. 12(7), 1713–1723 (2017)
Rao, R.: Wavelet Transforms. Encyclopedia of Imaging Science and Technology. Wiley, January 2002. https://doi.org/10.1002/0471443395.img112
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. arXiv:1512.03385v1 [cs.CV] 10 December 2015
Zhiwei, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks. In: Proceedings of IAPR International Conference on Biometrics (ICB), Beijing, China, pp. 26–31 (2012)
Chinese Academy of Sciences (CASIA), Institute of Automation, Face antispoofing dataset. http://www.cbsr.ia.ac.cn/english/FASDB_Agreement/Agreement.pdf
Pentland, A., Choudhury, T.: Face recognition for smart environments. Computer 33(2), 50–55 (2000)
Phillips, P.J., Martin, A., Wilson, C.L., Przybocki, M.: An introduction to evaluating biometric systems. Computer 33(2), 56–63 (2000)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)
Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)
Lee, T.S.: Image representation using 2D Gabor wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18(10), 959–971 (1996)
Spencer, J., Lawrence, D., Roy, K., Chatterjee, P., Esterline, A., Kim, J.: Presentation attack detection using convolutional neural networks and local binary patterns. In: First International Conference on Pattern Recognition and Artificial Intelligence, Montreal, Canada, 14–17 May 2018, pp. 529–534 (2018)
Chatterjee, P., Roy, K.: Anti-spoofing approach using deep convolutional neural network. In: Recent Trends and Future Technology in Applied Intelligence, January 2018. https://doi.org/10.1007/978-3-319-92058-0_72
Mordvintsev, A., Abid, K.: Face Detection using Haar Cascades. [online] OpenCV Tutorial (2013). https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_objdetect/py_face_detection/py_face_detection.html. Accessed 2018
Acknowledgment
This research is based upon work supported by the Science & Technology Center: Bio/Computational Evolution in Action Consortium (BEACON) and the Army Research Office (Contract No. W911NF-15-1-0524).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chatterjee, P., Yalchin, A., Shelton, J., Roy, K., Yuan, X., Edoh, K.D. (2019). Presentation Attack Detection Using Wavelet Transform and Deep Residual Neural Net. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11637. Springer, Cham. https://doi.org/10.1007/978-3-030-24900-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-24900-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24899-4
Online ISBN: 978-3-030-24900-7
eBook Packages: Computer ScienceComputer Science (R0)