Abstract
Face recognition is a most widely used and rapidly growing biometric technology. Lot of research has been done in this field, due to its significant applications in various sectors and their influence in our daily life such as securing financial transactions, information security, personal identification and surveillance systems. But face recognition systems are permeable to spoofing attack. The problem of spoofing can be minimized by detecting face liveness which is the main area of concern. Most researchers utilized only eyeblink as liveness indicator to detect face liveness. A novel face liveness detection algorithm with multiple liveness indicators has been proposed in this paper. Eyeblink sequence, lip movement and chin movement are the multiple liveness indicators that have been considered for reliable face liveness detection. Experimental results show that, the proposed method in conjunction with multiple liveness indicators significantly improves the security of face recognition system. The proposed method achieves higher liveness detection rate by detecting photo attack, eye-mouth photo imposter attack and video attack.
Similar content being viewed by others
References
Zhao, W., Chapella, R., Rosenfeld, A., & Phillips, P. J. (2000). Face recognition a literature survey. Technical Report CS-TR-4167, Center For Automation Research. University of Maryland.
Marasco, E., & Arun, R. (2014). A survey on anti-spoofing schemes for fingerprint recognition systems. ACM Computing Survey, 47(2), 36.
Kaehm, O., & Damer, N. (2012). 2D face liveness detection. In International conference of biometrics special interest group (BIOSIG).
Frischholz, R. W., & Werner. (2003). Avoiding replay-attacks in a face recognition system using head-pose estimation. In IEEE international workshop on analysis and modeling of faces and gestures (AMFG’03) (pp. 234–235).
Pan, G., Wu, Z., & Sun, L. (2008). Liveness detection for face recognition, recent advances in face recognition. In K. Delac, M. Grgic & M. S. Bartlett (Eds.), ISBN: 978-953-7619-34-3, InTech.
Lagorio, A., Tistarelli, M., Cadoni, M., Fookes, C., Clinton, B., & Sridha, S. (2013). Liveness detection based on 3D face shape analysis. In Proceedings of the 2013 international workshop on biometrics and forensics (IWBF), IEEE, Lisbon, Portugal (pp. 1–4).
Moeini, A., et al. (2017). Open-set face recognition across look-alike faces in real-world scenarios. Image and Vision Computing, 57, 1–14.
Bao, W., Li, H., Li, N., & Jiang, W. (2009). A liveness detection method for face recognition based on optical flow field. In IEEE international conference on image analysis and signal processing IASP (pp. 233–236).
Sun, L., Pan, G., Wu, Z., & Lao, S. (2007). Blinking-based live face detection using conditional random fields, In International Conference on ICB 2007, Seoul, Korea (pp. 252–260). August 27–29, 2007.
Kim, G., Eum, S., Suhr, J. K., Kim, D. I., Park, K. R., & Kim, J. (2012). Face liveness detection based on texture and frequency analysis. In 5th IAPR international conference on biometrics (ICB), New Delhi, India (pp. 67–72).
Tirunagari, S., et al. (2015). Detection of face spoofing using visual dynamics. IEEE Transactions on Information Forensics and Security, 10(4), 762–777.
Pan, G., et al. (2007). Eyeblink-based anti-spoofing in face recognition from a generic webcamera. ISBN: 978-1-4244-1631-8/07/$25.00. IEEE.
Anjos, A., & Marcel, S. (2011). Counter-measures to photo attacks in face recognition: A public database and a baseline. In International joint conference on biometrics 2011, October, 2011, Washington, DC, USA.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Singh, M., Arora, A.S. A Novel Face Liveness Detection Algorithm with Multiple Liveness Indicators. Wireless Pers Commun 100, 1677–1687 (2018). https://doi.org/10.1007/s11277-018-5661-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-018-5661-1