A novel iris texture extraction scheme for iris presentation attack detection

Abstract

Iris recognition systems suffer from a new challenge brought by various textured contact lenses, as they can change the appearance of iris texture. To deal with this challenge, conventional methods use Gray-Gradient Matrix and Gray-Level Run-Length Matrix (GLRLM) to extract iris texture features, and use Support Vector Machines (SVM) for authenticity classification. These methods only pay attention to the statistical value of feature matrix, but they ignore the details of texture features and isolate inherent connections between these texture details. This paper reveals that the intrinsic connection of iris texture features under the large scale of the features of neural networks is highly valuable for effectively eliminating the interference of textured contact lenses. Under this premise, we propose a novel iris anti-counterfeit detection method based on an improved Gray Level Co-occurrence Matrix (Modified-GLCM) combined with a binary classification neural network. The experimental results show that the proposed method outperforms the conventional texture analysis methods using feature statistical characteristics and the best result of LivDet-Iris-2017. What’s more, we analysis and verify the potential threat of the iris adversarial sample on the iris presentation attack detection algorithm through iris texture extraction.