Local binary hexagonal extrema pattern (LBHXEP): a new feature descriptor for fake iris detection

Abstract

Security 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, different types of spoofing attacks are used to violate the security of a biometric system. Biometrics liveness detection system used to recognize persons in a fast and trustworthy way through the use of unique biological distinctiveness. Presentation of a manufactured article of a human iris in the form of photo attack and contact lens attack could hamper the projected policy of a biometric system. The quality of real and fake iris images shows different textural characteristics. In this paper, we have proposed a novel and proficient feature descriptor, i.e., local binary hexagonal extrema pattern for fake iris detection. The proposed descriptor exploits the relationship between the center pixel and its Hexa neighbor. Hexagonal shape using “six-neighbor approach” is preferable to the rectangular structure due to its higher symmetry, consistent connectivity, and efficient use of space. The proposed consideration also solves the “curse of dimensionality” problem in liveness detection. The proposed descriptor is evaluated on ATVS-FIr DB and IIIT-D CLI databases for iris liveness detection and show promising performance for liveness detection in terms green, brown, etc. of accuracy and average error rate.