Micro Stripes Analyses for Iris Presentation Attack Detection

TL;DR

This paper introduces a lightweight micro-stripe analysis framework for iris presentation attack detection, demonstrating superior accuracy and reduced confusion between attack and genuine contact lens presentations across multiple databases.

Contribution

The paper proposes a novel micro-stripe analysis method that enhances iris attack detection by modifying segmentation and employing a majority vote classification approach.

Findings

Outperforms state-of-the-art algorithms in attack detection accuracy.

Reduces confusion between textured contact lenses and genuine iris images.

Validated on five diverse iris databases, including LivDet-2017.

Abstract

Iris recognition systems are vulnerable to the presentation attacks, such as textured contact lenses or printed images. In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures. In this procedure, a standard iris segmentation is modified. For our presentation attack detection network to better model the classification problem, the segmented area is processed to provide lower dimensional input segments and a higher number of learning samples. Our proposed Micro Stripes Analyses (MSA) solution samples the segmented areas as individual stripes. Then, the majority vote makes the final classification decision of those micro-stripes. Experiments are demonstrated on five databases, where two databases (IIITD-WVU and Notre Dame) are from the LivDet-2017 Iris competition. An in-depth experimental evaluation of this framework reveals a superior performance compared with state-of-the-art algorithms. Moreover, our solution minimizes the confusion between textured (attack) and soft (bona fide) contact lens presentations.