Vefak Murat AKMAN, REVNA ACAR VURAL, Kemal Talha KOÇ

Touchless Authentication System Using Visual Fingertip Trajectories

Biometric traces which are left on the screen of a security device or a mobile phone may cause a threat for the privacy of people when such personal data is likely to be stolen. In order to overcome this problem, a touchless authentication system is proposed which allows the usage of fingertip trajectories for creating or entering personal information such as password. First, fingertip regions are detected with Haarcascade method and these regions are tracked using Lucas-Kanade algorithm. The accurate detection of fingertip with invariance to pose, lightning and brightness is essential and crucial for security. Experimental results show that the proposed system proved its efficiency in various backgrounds where the finger is oriented in various directions. Having low computational effort, this system is suitable for low cost and real time processing.

PDF

___

1. P. Viola and M. Jones “Rapid Object Detection using a Boosted Cascade of Simple Features,” IEEE Conf. Computer Vision and Pattern Recognition (CVPR), vol. 1, Dec. 2001.

2. R.M. Gurav, P.K. Kadbe, “Real time Finger Tracking and Contour Detection for Gesture Recognition using OpenCV,” Int. Conf. Industrial Instrumentation and Control (ICIC), pp. 974-977, May 2015.

3. Y. Chunxuan, W. Jingtao, “Finger-Fist Detection in First-Person View based on Monocular Vision using Haar-Like Features” 33rd Chinese Control Conf., pp. 4920-4923, June 2014.

4. Y. Ma, X. Ding, “Robust Real-Time Face Detection Based on Cost-Sensitive AdaBoost Method,” Int. Conf. Multimedia and Expo, ICME’03 Proc., vol. 2, pp. 465-472, 2003.

5. Y. Freund, R.E. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” J. Computer and Systems Sciences, vol. 55, pp. 119-139, 1997.

6. K. Tieu, P. Viola, “Boosting Image Retrieval,” IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 228-235, 2000.

7. E. ul Haq, S.J.H. Pirzada, M.W. Bing, “New Hand Gesture Recognition Method for Mouse Operations,” IEEE 54th Int. Midwest Symp. Circuits and Systems, pp. 1-4, Aug. 2011.

8. D. Popa, V. Gui, M. Otesteanu, “Real-Time Finger Tracking with Improved Performance in Video Sequences with Motion Blur,” 38th Int. Conf. Telecommunications and Signal Processing (TSP), pp. 1-6, June 2015.

9. R. Agrawal, N. Gupta, “Real Time Hand Gesture Recognition for Computer Interaction,” IEEE 6th Int. Conf. Advanced Computing, pp. 73-77, Feb. 2014.

10. R. Meena Prakash, T. Deepa, T. Gunasundari, N. Kasthuri, “Gesture Recognition and Finger Tip Detection for Human Computer Interaction,” Int. Conf. Innovations in Information, Embedded and Communication Systems, pp. 1-4, March 2017.

11. G. Simion, V. Gui, M. Otesteanu, “Finger Detection Based on Hand Contour and Colour Information,” 6th IEEEInt. Symp. Applied Computational Intelligence and Informatics (SACI), pp. 97-100, May 2011.

12. S.K. Kang, M.Y. Nam, P.K. Rhee, “Color Based Hand and Finger Detection Technology for User Interaction,” Int. Conf. Convergence Hybrid Information Technology, pp. 229-236, 2008.

13. C. Joniez, E. Monari, C. Qui, “Towards Touchless Palm and Finger Detection for Fingerprint Extraction with Mobile Devices,” Int. Conf. Biometrics Special Interest Group, pp. 1-8, Sep. 2015.

14. G. Wu, W. Kang, “Robust Fingertip Detection in a Complex Environment,” IEEE Trans. Multimedia, vol. 18, no. 6, pp. 978-987, June 2016.

15. Y. Huang, X. Liu, L. Jin, X. Zhang, “DeepFinger: A Cascade Convolutional Neuron Network Approach to Finger Key Point Detection in Egocentric Vision with Mobile Camera,” IEEE Int. Conf. Systems, Man, and Cybernetics, pp. 2944-2949, 2015.

16. Crowley James L., F. Berard, J. Coutaz, “Finger Tracking as an Input Device for Augmented Reality,” Int. Workshop on Face and Gesture Recognition, June 1995.

17. D. Lee, Y. Park, “Vision-based Remote-Control System by Motion Detection and Open Finger Counting,” IEEE Trans. Consumer Electronics, vol. 55, pp. 2308-2313, Nov. 2009.

18. B.D. Lucas, T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” Proc. the 7th Int. Joint Conf. Artificial Intelligence, pp. 674-679, Apr. 1981.

19. G. Farnebäck, “Two-Frame Motion Estimation Based on Polynomial Expansion,” Proc. the 13th Scandinavian Conf. Image Analysis, pp. 363-370, June 2003.

20. G. Wu, W. Kang, “Vision-Based Fingertip Tracking Utilizing Curvature Points Clustering and Hash Model Representation,” IEEE Trans. Multimedia, vol. 19, pp. 1730-1741, Apr. 2017.

21. L. Yu, “Moving Target Tracking Based on Improved MeanShift And Kalman Filter Algorithm,” 13th IEEE Conf. Industrial Electronics and Applications, pp. 2486-2490, May 2018.

22. K. Du, Y. Ju, Y. Jin, G. Li, S. Qian, Y. Li, “MeanShift Tracking Algorithm with Adaptive Block Color Histogram,” 2nd Int Conf. Consumer Electronics, Communications and Networks, pp. 2692-2695, Apr. 2012.

23. G.R. Bradski, “Real Time Face and Object Tracking as a Component of a Perceptual User Interface,” Proc. IEEE Workshop on Applications of Computer Vision, pp. 214-219, Oct. 1998.

24. C. Xiu, X. Su, X. Pan, “Improved Target Tracking Algorithm Based on Camshift,” Chinese Control and Decision Conf., pp. 4449-4454, June 2018.

25. L. Li, Y. Luo, “Improved Video Moving Target Tracking Based on Camshift,” Am. J Computational Math., vol. 6, no. 4, pp. 357-364, Jan. 2016.

26. X. Pan, Z. Ling, A. Pingley, W. Yu. N. Zhang, K. Ren, X. Fu, “Password Extraction via Reconstructed Wireless Mouse Trajectory,” IEEE Trans. Dependable and Secure Computing, vol. 13, pp. 461-473, March 2015.