Kelvin S. Bryant
ABSTRACT
In this paper, we demonstrate how to reduce the chance of a spoofed iris code being falsely accepted by an iris recognition system. We simulate the system attack by taking one of the registered iris codes from a subject set and mutating it by several different rates and presenting the resultant iris codes to our system. Our approach uses the k-nearest neighbors from a training set to the known spoof to establish a critical distance. Presented iris codes from our mutant set that have a Hamming Ratio when compared to the spoof that is less than the critical distance are rejected. Those that are falsely accepted are totaled to produce a Spoof False Accept Rate (SP-FAR). The second phase of our approach uses traditional iris code recognition to reduce the SP-FAR by rejecting those spoofs that were mutated to a degree such that they will not match any of the other iris codes in the training set.
- Bowyer, K. W., Hollingsworth, K. P., and Flynn, P. J. (2008). "Image Understanding for Iris Biometrics: A Survey", Computer Vision and Image Understanding, 110:281--307, 2008. Google Scholar
Digital Library
- Daugman, J. 2004. "How iris recognition works," IEEE Transactions on Circuits and Systems for Video Technology, 14(1):21--30, 2004. Google ScholarDigital Library
- Daugman, J. 2008. "Iris Recognition," Handbook of Biometrics, Anil K. Jain, Patrick Flynn, Arun A. Ross, Eds., pp. 71--90, Springer.Google Scholar
- Dozier, G. V., Savvides, M., Bryant, K., Munemoto, T., Ricanek, K., Jr., Woodard, D. (2009 "Developing Iris Templates via Bit Inconsistency and GRIT," Encyclopedia of Biometrics, Marios Savvides, Ed., Springer.Google Scholar
- Dozier, G., Frederiksen, K., Meeks, R., Savvides, M., Bryant, K., Hopes, D., Munemoto, T. (2009). "Minimizing the Number of Bits Needed for Iris Recognition via Bit Inconsistency and GRIT," Proceedings of the 2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, pp. 30--37, Nashville, March 30 April 2nd, 2009.Google Scholar
- National Institute of Standards and Technology Iris Challenge Evaluation, 2006, http://iris.nist.gov/ice.Google Scholar
- Thornton, Savvides, M., and Kumar, V. (2005). Robust Iris Recognition Using Advanced Correlation Techniques," Proc. Int'l Conf. Image Analysis and Recognition, pp. 1098--1105, 2005. Google ScholarDigital Library
- Nixon, K. A, Aimale, V., and Rowe, R. K. (2008). Spoof "Detection Schemes," Handbook of Biometrics, pp. 403--423, Springer.Google ScholarCross Ref
Index Terms
- A two-phased approach to reducing the false accept rate of spoofed iris codes
Recommendations
Cross-Sensor Iris Recognition through Kernel Learning
Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, ...
Local binary hexagonal extrema pattern (LBHXEP): a new feature descriptor for fake iris detection
AbstractSecurity agencies frequently use biometric traits for automatic recognition of a person. The human iris is the most hopeful biometric authentication that can accurately identify a person from their exclusive features. However, in recent years, ...
Multimodal biometric system for ECG, ear and iris recognition based on local descriptors
AbstractCombination of multiple information extracted from different biometric modalities in multimodal biometric recognition system aims to solve the different drawbacks encountered in a unimodal biometric system. Fusion of many biometrics has proposed ...
Comments