Palmprint spoofing detection by using deep learning technique on Multispectral database
Keywords:
Biometric, Palmprint, spoofing, Image, databaseAbstract
The protection of Biometric systems against attacks is crucial as biometric devices proliferate in the field of personal authentication. The presentation assault is the most prevalent type of attack on biometric systems; it entails presenting a fake copy (artefact) of the true biometric to the sensor in order to gain unauthorised access. The vulnerability in palmprint-based biometric systems has not received much attention despite the substantial threat posed by these assaults. In this research, we show how to detect a spoof palmprint image. Spoofing attacks involving faked images pose a significant threat to biometric systems. For the suggested method, we use the CASIA palmprint database, from which we constructed our own spoof database using printed photos. After that, we did some pre-processing to obtain the ROI image and a noise-free image for feature extraction using the SIFT approach. We use the convolution neural network for classification and the SVM for comparison. We obtained a result of 96.2% for our proposed palmprint system identification and 89% for SVM. But our main goal is to train the model for spoof detection, so we take some normal images and some spoof images for our train model and use the confusion matrix to calculate the accuracy of our model. We obtain an overall accuracy of 86% for our spoof detection by computing the confusion matrix.
References
. Ratha, N. K., Connell, J. H., & Bolle, R. M. (2001). An analysis of minutiae matching strength. In Audio-and Video-Based Biometric Person Authentication: Third International Conference, AVBPA 2001 Halmstad, Sweden, June 6–8, 2001 Proceedings 3 (pp. 223-228). Springer Berlin Heidelberg.
. Chakka, M. M., Anjos, A., Marcel, S., Tronci, R., Muntoni, D., Fadda, G., ... & Pietikäinen, M. (2011, October). Competition on counter measures to 2-d facial spoofing attacks. In 2011 International Joint Conference on Biometrics (IJCB) (pp. 1-6). IEEE.
. Kollreider, K., Fronthaler, H., & Bigun, J. (2005, October). Evaluating liveness by face images and the structure tensor. In Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) (pp. 75-80). IEEE.
. Kollreider, K., Fronthaler, H., & Bigun, J. (2008, June). Verifying liveness by multiple experts in face biometrics. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 1-6). Ieee.
. Galbally, J., Marcel, S., & Fierrez, J. (2013). Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. IEEE transactions on image processing, 23(2), 710-724.
. Patil, I., Bhilare, S., & Kanhangad, V. (2016, February). Assessing vulnerability of dorsal hand-vein verification system to spoofing attacks using smartphone camera. In 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) (pp. 1-6). IEEE.
. Shen, C., Chen, Y., & Yang, G. (2016, February). On motion-sensor behavior analysis for human-activity recognition via smartphones. In 2016 Ieee International Conference on Identity, Security and Behavior Analysis (Isba) (pp. 1-6). IEEE.
. Biggio, B., Fumera, G., Marcialis, G. L., & Roli, F. (2016). Statistical meta-analysis of presentation attacks for secure multibiometric systems. IEEE transactions on pattern analysis and machine intelligence, 39(3), 561-575.
. Sajjad, M., Khan, S., Hussain, T., Muhammad, K., Sangaiah, A. K., Castiglione, A., ... & Baik, S. W. (2019). CNN-based anti-spoofing two-tier multi-factor authentication system. Pattern Recognition Letters, 126, 123-131.
. Raghavendra, R., & Busch, C. (2015). Robust scheme for iris presentation attack detection using multiscale binarized statistical image features. IEEE Transactions on Information Forensics and Security, 10(4), 703-715.
. Reddy, P. V., Kumar, A., Rahman, S. M. K., & Mundra, T. S. (2007, September). A new method for fingerprint antispoofing using pulse oxiometry. In 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems (pp. 1-6). IEEE.
. Parthasaradhi, S. T., Derakhshani, R., Hornak, L. A., & Schuckers, S. A. (2005). Time-series detection of perspiration as a liveness test in fingerprint devices. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(3), 335-343.
. Erdogmus, N., & Marcel, S. (2014). Spoofing face recognition with 3D masks. IEEE transactions on information forensics and security, 9(7), 1084-1097.
. Hadid, A. (2014). Face biometrics under spoofing attacks: Vulnerabilities, countermeasures, open issues, and research directions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 113-118).
. Zhang, H., Sun, Z., Tan, T., & Wang, J. (2011). Learning hierarchical visual codebook for iris liveness detection. In International Joint Conference on Biometrics (Vol. 1).
. Raghavendra, R., & Busch, C. (2014, September). Presentation attack detection algorithm for face and iris biometrics. In 2014 22nd European signal processing conference (EUSIPCO) (pp. 1387-1391). IEEE.
. Sanchez, J., Saratxaga, I., Hernaez, I., Navas, E., Erro, D., & Raitio, T. (2015). Toward a universal synthetic speech spoofing detection using phase information. IEEE Transactions on Information Forensics and Security, 10(4), 810-820.
. Galbally, J., Marcel, S., & Fierrez, J. (2014). Biometric antispoofing methods: A survey in face recognition. IEEE Access, 2, 1530-1552.
. Reddy, P. V., Kumar, A., Rahman, S. M. K., & Mundra, T. S. (2008). A new antispoofing approach for biometric devices. IEEE transactions on biomedical circuits and systems, 2(4), 328-337.
. Zhang, D., Guo, Z., Lu, G., Zhang, L., & Zuo, W. (2009). An online system of multispectral palmprint verification. IEEE transactions on instrumentation and measurement, 59(2), 480-490.
. Li, W., Zhang, L., Zhang, D., Lu, G., & Yan, J. (2010, June). Efficient joint 2D and 3D palmprint matching with alignment refinement. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 795-801). IEEE.
. Kose, N., & Dugelay, J. L. (2013, April). Countermeasure for the protection of face recognition systems against mask attacks. In 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) (pp. 1-6). IEEE.
. Pan, G., Sun, L., Wu, Z., & Lao, S. (2007, October). Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In 2007 IEEE 11th international conference on computer vision (pp. 1-8). IEEE.
. Faraj, M. I., & Bigun, J. (2007). Audio–visual person authentication using lip-motion from orientation maps. Pattern recognition letters, 28(11), 1368-1382.
. 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.
. Gragnaniello, D., Sansone, C., & Verdoliva, L. (2015). Iris liveness detection for mobile devices based on local descriptors. Pattern Recognition Letters, 57, 81-87.
. Kanhangad, V., & Kumar, A. (2013, December). Securing palmprint authentication systems using spoof detection approach. In Sixth International Conference on Machine Vision (ICMV 2013) (Vol. 9067, pp. 321-325). SPIE.
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