Biometric authentication is used in Smart devices to lock and unlock devices instead of a PIN. However, using devices in uncontrolled environments is highly susceptible to spoofing by replay attacks. As an example of biometrics is a static face authentication, attackers use face images of spoofed persons to unlock devices, which leads to insecure operations and lack of confidential information without the owner’s knowledge. We have proposed a novel dynamic biometric authentication system (DBAS) based on capturing person eye-blinking movements of a person in addition to face authentication instead of using static face authentication. Proposed DBAS take a sequence of using images as input by asking the user to follow an eye-blink sequence. Then proposed DBAS unlock the mobile devices based on the correct eye-blink sequence made by the person. The test results show that our method is effective and robust for the screen unlocks on smart devices with 98.4% accuracy.
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Jain, A. K., Ross, A., & Pankanti, S. (2006). Biometrics: A tool for information security. IEEE Transactions on Information Forensics and Security,1(2), 125–143.
Ratha, N. K., Connell, J. H., & Bolle, R. M. (2001). An analysis of minutiae matching strength. Audio- and Video-Based Biometric Person Authentication (AVBPA),2091, 223–228.
Singh, S., & Prasad, S. V. A. V. (2018). Techniques and challenges of face recognition: A critical Review. Procedia Computer Science,143, 536–543.
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Computing Surveys,35(4), 399–458.
Komulainen, J., Hadid, A., & Pietikainen, M. (2013). Context based face anti-spoofing. In IEEE 6th international conference on biometrics: theory, applications and systems (pp. 1–8).
Rogmann, N., & Lee, M. K. (2015). Liveness detection in biometrics. In International conference of the biometrics special interest group (BIOSIG) (pp. 1–14).
Maatta, J., Hadid, A., & Pietiktiinen, M. (2011). Face spoofing detection from single images using micro-texture analysis. In International joint conference on biometrics (IJCB) (pp. 1–7).
Tang, D., Zhou, Z., Zhang, Y., & Zhang, K. (2018). Face flashing: A secure liveness detection protocol based on light reflections. In Network and distributed systems security (NDSS) symposium.
Saad, A. M. K. (2015). Anti-spoofing using challenge-response user interaction (pp. 1–72). New York: American University in Cairo School of Sciences and Engineering.
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., et al. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics,8(3), 1–66.
Kim, K. W., Hong, H. G., Nam, G. P., & Park, K. R. (2017). A study of deep cnn-based classification of open and closed eyes using a visible light camera sensor. Sensors,17(7), 1–21.
Han, Y. J., Kim, W., & Park, J. S. (2018). Efficient eye-blinking detection on smartphones: A hybrid approach based on deep learning. Mobile Information Systems,2018, 1–8.
King, D. E. (2009). Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research,10, 1755–1758.
Alhaq, A., & Al-Shamery, E. (2018). A new deep neural network regression predictor based stock market. Journal of Engineering and Applied Sciences,13(5), 4794–4801.
Albelwi, S., & Mahmood, A. (2017). A framework for designing the architectures of deep convolutional neural networks. Entropy,19(6), 242.
Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR).
Geitgey, A. (2016). Medium. [Online]. Available: https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78. Accessed 4 December 2019.
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering (pp. 815–823).
Kazemi, V., & Sullivan, J. (2014). One millisecond face alignment with an ensemble of regression trees.
Soukupova, T., & Cech, J. (2016). Real-time eye blink detection using facial landmarks. Computer Vision Winter Workshop,3(5), 1–8.
Huang, G. B., Matta, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments (pp. 1–11). Boston: University of Massachusetts.
Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision (IJCV),57(2), 137–154.
Tan, X., Song, F., Zhou, Z. H., & Chen, S. (2009) Enhanced pictorial structures for precise eye localization under uncontrolled conditions. In Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR’09) (pp. 1621–1628).
Song, F., Tan, X., Liu, X., & Chen, S. (2014). Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recognition,47(9), 2825–2838.
Pan, G., Sun, L., Wu, Z., & Lao, S. (2007). Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In 2007 IEEE 11th international conference on computer vision (pp. 1–8).
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift (Vol. 3, pp. 1–11). arXiv:1502.03167[cs.LG].
Schilling, F. (2016). The effect of batch normalization on deep convolutional neural networks, Stockholm.
Cyril, G., & Gaussier, E. (2005). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation (Vol. 3408, pp. 345–359).
Chu, C.-H., & Feng, Y.-K. (2018). Study of eye blinking to improve face recognition for screen unlock on mobile devices. Journal of Electrical Engineering and Technology,13(2), 953–960.
Sridhathan, C., Senthil Kumar, M., & Arutselvan, K. (2018). Unlocking mobile devices using improved face recognition and eye blinking technique. International Journal of Applied Engineering Research,13(24), 16907–16909.
Noman, M., Bin, T., Ahad, M., & Rahman, A. (2018). Mobile-based eye-blink detection performance analysis on android platform. Frontiers,5, 4.
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Saied, M., Elshenawy, A. & Ezz, M.M. A Novel Approach for Improving Dynamic Biometric Authentication and Verification of Human Using Eye Blinking Movement. Wireless Pers Commun (2020). https://doi.org/10.1007/s11277-020-07601-x
- Face recognition
- Classification of open and closed eyes
- Presentation attack detection