CFP last date
20 January 2025
Reseach Article

Detection and Spoofing Methods of Face Recognition using Visualization Dynamics: A Review

by Mandeep Kaur, Hanit Karwal, Kulvinder Singh Mann
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 45
Year of Publication: 2019
Authors: Mandeep Kaur, Hanit Karwal, Kulvinder Singh Mann
10.5120/ijca2019919368

Mandeep Kaur, Hanit Karwal, Kulvinder Singh Mann . Detection and Spoofing Methods of Face Recognition using Visualization Dynamics: A Review. International Journal of Computer Applications. 178, 45 ( Sep 2019), 40-44. DOI=10.5120/ijca2019919368

@article{ 10.5120/ijca2019919368,
author = { Mandeep Kaur, Hanit Karwal, Kulvinder Singh Mann },
title = { Detection and Spoofing Methods of Face Recognition using Visualization Dynamics: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2019 },
volume = { 178 },
number = { 45 },
month = { Sep },
year = { 2019 },
issn = { 0975-8887 },
pages = { 40-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number45/30854-2019919368/ },
doi = { 10.5120/ijca2019919368 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:53:13.693720+05:30
%A Mandeep Kaur
%A Hanit Karwal
%A Kulvinder Singh Mann
%T Detection and Spoofing Methods of Face Recognition using Visualization Dynamics: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 45
%P 40-44
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biometric systems have claimed to become one of the sore subjects in the present epoch when it comes to validation or recognition of an individual. Biometric system mainly focuses on identification of traits of an individual. The foundation of face recognition, globally, is laid on a set of unique and specific recognizable or valid data. This data can be in the form of digital images or video frames. In spite of being ubiquitous, face recognition data is prone to spoofing attacks as face recognition data introduces a high probability of breach allowing a fraudulent user to masquerade as a registered user to gain illegitimate access and privileges. It has, thereby, become highly unlikely to avoid the prevention of such frauds by developing reliable and robust methods. This paper intends to review and acknowledge numerous face detection ways and to sort them into totally different classes.

References
  1. Gressel, C. D. (2001). U.S. Patent No. 6,311,272. Washington, DC: U.S. Patent and Trademark Office.
  2. Negin, M., Chmielewski, T. A., Salganicoff, M., Von Seelen, U. M., Venetainer, P. L., and Zhang, G. G. (2000). An iris biometric system for public and personal use. Computer, vol 33(2), pp. 70-75.
  3. Gamboa, H., and Fred, A. (2004, August). A behavioral biometric system based on human-computer interaction. In Biometric Technology for Human Identification (Vol. 5404, pp. 381-393). International Society for Optics and Photonics.
  4. Lee, J. C. (2012). A novel biometric system based on palm vein image. Pattern Recognition Letters, vol 33(12),pp. 1520-1528.
  5. Määttä, J., Hadid, A., and Pietikäinen, M. (2011, October). Face spoofing detection from single images using micro-texture analysis. In 2011 international joint conference on Biometrics (IJCB) (pp. 1-7). IEEE.
  6. Erdogmus, N., and Marcel, S. (2013, September). Spoofing in 2d face recognition with 3d masks and anti-spoofing with kinect. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS) (pp. 1-6). IEEE.
  7. Boulkenafet, Z., Komulainen, J., and Hadid, A. (2016). Face spoofing detection using colour texture analysis. IEEE Transactions on Information Forensics and Security, vol 11(8), pp. 1818-1830.
  8. De Marsico, M., Nappi, M., Riccio, D., and Dugelay, J. L. (2012, March). Moving face spoofing detection via 3D projective invariants. In 2012 5th IAPR International Conference on Biometrics (ICB) (pp. 73-78). IEEE.
  9. Chingovska, I., Anjos, A., and 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.
  10. Komulainen, J., Hadid, A., Pietikäinen, M., Anjos, A., and Marcel, S. (2013, June). Complementary countermeasures for detecting scenic face spoofing attacks. In 2013 International conference on biometrics (ICB) (pp. 1-7). IEEE.
  11. Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., and Li, S. Z. (2012, March). A face antispoofing database with diverse attacks. In 2012 5th IAPR international conference on Biometrics (ICB) (pp. 26-31). IEEE.
  12. De Freitas Pereira, T., Anjos, A., De Martino, J. M., and Marcel, S. (2012, November). LBP− TOP based countermeasure against face spoofing attacks. In Asian Conference on Computer Vision (pp. 121-132). Springer, Berlin, Heidelberg.
  13. Lu, Y., Zhou, J., and Yu, S. (2012). A survey of face detection, extraction and recognition. Computing and informatics, vol 22(2), pp. 163-195.
  14. Manjani, I., Tariyal, S., Vatsa, M., Singh, R., and Majumdar, A. (2017). Detecting silicone mask-based presentation attack via deep dictionary learning. IEEE Transactions on Information Forensics and Security, vol 12(7), pp. 1713-1723.
  15. Jayan, T. J., and Aneesh, R. P. (2018, July). Image Quality Measures Based Face Spoofing Detection Algorithm for Online Social Media. In 2018 International CET Conference on Control, Communication, and Computing (IC4) (pp. 245-249). IEEE.
  16. Patel, K., Han, H., and Jain, A. K. (2016). Secure face unlock: Spoof detection on smartphones. IEEE Transactions on Information Forensics and Security, vol 11(10), pp. 2268-2283.
  17. Fourati, E., Elloumi, W., and Chetouani, A. (2017, August). Face anti-spoofing with image quality assessment. In 2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART) (pp. 1-4). IEEE.
  18. Li, H., Wang, S., and Kot, A. C. (2016, December). Face spoofing detection with image quality regression. In 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). IEEE.
  19. Galbally, J., Marcel, S., and Fierrez, J. (2014). Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. IEEE transactions on image processing, vol 23(2), pp. 710-724.
  20. De Marsico, M., Nappi, M., Riccio, D., and Dugelay, J. L. (2012, March). Moving face spoofing detection via 3D projective invariants. In 2012 5th IAPR International Conference on Biometrics (ICB) (pp. 73-78). IEEE.
  21. Boulkenafet, Z., Komulainen, J., and Hadid, A. (2016). Face spoofing detection using colour texture analysis. IEEE Transactions on Information Forensics and Security, vol 11(8), pp. 1818-1830.
  22. Ramachandran, V., and Nandi, S. (2005, December). Detecting ARP spoofing: An active technique. In International Conference on Information Systems Security (pp. 239-250). Springer, Berlin, Heidelberg.
  23. Bao, W., Li, H., Li, N. and Jaing, W. (2009). A liveness detection method for face recognition based on optical flow field. International Conference on Image Analysis and Signal Processing, pp. 233-236. IEEE.
  24. Chakraborty, S., and Das, D. (2014). An overview of face liveness detection. arXiv preprint arXiv:1405.2227.
  25. Wen, D., Han, H., and Jain, A. K. (2016). Face spoof detection with image distortion analysis. IEEE Transactions on Information Forensics and Security, vol 10(4), pp. 746-761.
  26. VS, S. and Linda, M. A Survey on Facial Spoofing Detection. International Journal of Science and Technology Research, vol 5(1), pp. 49-53.
Index Terms

Computer Science
Information Sciences

Keywords

Biometrics Face spoofing Spoofing Attacks 2D Face Recognition Detection ways Visualization Dynamics.