Abstract
Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural network-featured method for detecting presentation attacks. Full Text: PDF ReferencesS.R. Arashloo, J. Kittler, W. Christmas, "Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features", IEEE Trans. Inf. Forensics Secur. 10, 11 (2015). CrossRef A. Anjos, M.M. Chakka, S. Marcel, "Motion-based counter-measures to photo attacks inface recognition", IET Biometrics 3, 3 (2014). CrossRef M. Killioǧlu, M. Taşkiran, N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking", Proc. SAMI IEEE, (2017). CrossRef A. Asaduzzaman, A. Mummidi, M.F. Mridha, F.N. Sibai, "Improving facial recognition accuracy by applying liveness monitoring technique", Proc. ICAEE IEEE, (2015). CrossRef M. Kowalski, "A Study on Presentation Attack Detection in Thermal Infrared", Sensors 20, 14 (2020). CrossRef C. Galdi, et al, "PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study", IET Biometrics 9, 6 (2020). CrossRef D.A. Socolinsky, A. Selinger, J. Neuheisel, "Face recognition with visible and thermal infrared imagery", Comput. Vis Image Underst. 91, 1-2 (2003) CrossRef L. Sun, W. Huang, M. Wu, "TIR/VIS Correlation for Liveness Detection in Face Recognition", Proc. CAIP, (2011). CrossRef J. Seo, I. Chung, "Face Liveness Detection Using Thermal Face-CNN with External Knowledge", Symmetry 2019, 11, 3 (2019). CrossRef A. George, Z. Mostaani, D Geissenbuhler, et al., "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network", IEEE Trans. Inf. Forensics Secur. 15, (2020). CrossRef S. Ren, K. He, R. Girshick, J. Sun, "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition", Proc. CVPR IEEE 39, (2016). CrossRef K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", Proc. CVPR, (2016). CrossRef K. Mierzejewski, M. Mazurek, "A New Framework for Assessing Similarity Measure Impact on Classification Confidence Based on Probabilistic Record Linkage Model", Procedia Manufacturing 44, 245-252 (2020). CrossRef
Highlights
Face recognition systems are being challenged by several diverse malicious attacks
More advanced presentation attack detection (PAD) methods are being developed in response t o n ew presentation attacks introduced
The aim of this paper is to present the outcomes of research into detection of various face presentation attacks in thermal infrared
Summary
Face recognition systems are being challenged by several diverse malicious attacks. One of themost popular nowadays exploits someoneelse’s identity p resented t o the biometric sensor. A variety of presentation attack detection methods have been proposed, mainly operating in the visible light domain [1,2,3,4] Another pertinent attack predictor might be the display of subject heat emission, which is a distinctive quality of thermal infrared [5,6,7]. The proposed method was reported to obtain an Average Classification Error Rate(ACER) of 2.59 % a nd 0 .8 4% for thermal imaging and a combination of Grayscale, Depth, Infrared, and Thermal, respectively. This m et hod obtains high results when using several channels together and should not be considered in a single-channel configuration
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