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BioMed Research International
Volume 2015 (2015), Article ID 343475, 8 pages
http://dx.doi.org/10.1155/2015/343475
Research Article

A Multimodal User Authentication System Using Faces and Gestures

1School of Electrical Engineering and Computer Science, Kyungpook National University, Deagu 702-701, Republic of Korea
2School of Computer Science and Engineering, Kyungpook National University, Deagu 702-701, Republic of Korea

Received 26 September 2014; Accepted 19 November 2014

Academic Editor: Sabah Mohammed

Copyright © 2015 Hyunsoek Choi and Hyeyoung Park. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Ross and A. K. Jain, “Multimodal biometrics: an overview,” in Proceedings of the 12th European Signal Processing Conference, pp. 1221–1224, Vienna, Austria, September 2004.
  2. K. Bowyer, K. Chang, and P. Yan, “Multi-modal biometrics: an overview,” in Proceedings of the 2nd Workshop on Multi-Modal User Authentication, Toulouse, France, May 2006.
  3. A. K. Jain and A. Kumar, “Biometric recognition: an overview,” in Second Generation Biometrics: The Ethical, Legal and Social Context, E. Mordini and D. Tzovaras, Eds., pp. 49–79, Springer, Amsterdam, The Netherlands, 2012. View at Google Scholar
  4. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399–458, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Jafri and H. R. Arabnia, “A survey of face recognition techniques,” Journal of Information Processing Systems, vol. 5, no. 2, pp. 41–68, 2009. View at Publisher · View at Google Scholar
  6. I. A. Kakadiaris, G. Passalis, T. Theoharis, G. Toderici, I. Konstantinidis, and N. Murtuza, “Multimodal face recognition: combination of geometry with physiological information,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 2, pp. 1022–1029, San Diego, Calif, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition,” Computer Vision and Image Understanding, vol. 101, no. 1, pp. 1–15, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. K. I. Chang, K. W. Bowyer, and P. J. Flynn, “An evaluation of multimodal 2D+3D face biometrics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 619–624, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. R. V. Yampolskiy and V. Govindaraju, “Behavioural biometrics: a survey and classification,” International Journal of Biometrics, vol. 1, no. 1, pp. 81–113, 2008. View at Publisher · View at Google Scholar
  10. J. Liu, Z. Wang, L. Zhong, J. Wickramasuriya, and V. Vasudevan, “uWave: accelerometer-based personalized gesture recognition and its applications,” in Proceedings of the 7th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom '09), pp. 1–9, Galveston, Tex, USA, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Bailador, C. Sanchez-Avila, J. Guerra-Casanova, and A. de Santos Sierra, “Analysis of pattern recognition techniques for in-air signature biometrics,” Pattern Recognition, vol. 44, no. 10-11, pp. 2468–2478, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Guerra-Casanova, C. Sánchez-Ávila, G. Bailador, and A. de Santos Sierra, “Authentication in mobile devices through hand gesture recognition,” International Journal of Information Security, vol. 11, no. 2, pp. 65–83, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Guse, Gesture-based user authentication on mobile devices using accelerometer and gyroscope [Master thesis], Berlin Institue of Technology, 2011.
  14. N. Sae-Bae, K. Ahmed, K. Isbister, and N. Memon, “Biometric-rich gestures: a novel approach to authentication on multi-touch devices,” in Proceedings of the 30th ACM Conference on Human Factors in Computing Systems (CHI '12), pp. 977–986, Austin, Tex, USA, May 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Lai, J. Konrad, and P. Ishwar, “Towards gesture-based user authentication,” in Proceedings of the IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance (AVSS '12), pp. 282–287, Beijing, China, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Wu, J. Konrad, and P. Ishwar, “The value of multiple viewpoints in gesture-based user authentication,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop, pp. 90–97, Columbus, Ohio, USA, June 2014.
  17. P. Viola and M. J. Jones, “Robust real-time face detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 886–893, San Diego, Calif, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Vedaldi and B. Fulkerson, “VLFeat: An Open and Portable Library of Computer Vision Algorithms,” http://www.vlfeat.org.
  20. H. Choi, J. Seo, and H. Park, “Matrix correlation distance for 2D image classification,” in Proceedings of the of ACM Symposium on Applied Computing, pp. 1741–1742, Gyeongju, Republic of Korea, March 2014. View at Publisher · View at Google Scholar
  21. M. Müller, “Dynamic time warping,” in Information Retrieval for Music and Motion, M. Müller, Ed., pp. 69–84, Springer, New York, NY, USA, 2007. View at Google Scholar
  22. ChaLearn, ChaLearn Gesture Dataset (CGD 2011), 2012, http://gesture.chalearn.org/data.
  23. A. Martin, G. Doddington, and T. Kamm, “The DET curve in assessment of detection task performance,” in Proceedings of the European Conference on Speech Communication and Technology, pp. 1895–1898, Rhodes, Greece, September 1997.