A Code-Level Approach to Heterogeneous Iris Recognition

• Published in: IEEE transactions on information forensics and security Vol. 12; no. 10; pp. 2373 - 2386
• Main Authors: Liu, Nianfeng, Liu, Jing, Sun, Zhenan, Tan, Tieniu
• Format: Journal Article
• Language: English
• Published: IEEE 01.10.2017
• Subjects: cross-sensor; heterogeneous; Image resolution; Image sensors; Iris recognition; Markov; Markov random fields; Probes; Robustness; Sensors
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2017.2686013

Matching heterogeneous iris images in less constrained applications of iris biometrics is becoming a challenging task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities or filtered features. In contrast, this paper proposes a code-level approach in heterogeneous iris recognition. The non-linear relationship between binary feature codes of heterogeneous iris images is modeled by an adapted Markov network. This model transforms the number of iris templates in the probe into a homogenous iris template corresponding to the gallery sample. In addition, a weight map on the reliability of binary codes in the iris template can be derived from the model. The learnt iris template and weight map are jointly used in building a robust iris matcher against the variations of imaging sensors, capturing distance, and subject conditions. Extensive experimental results of matching cross-sensor, high-resolution versus low-resolution and, clear versus blurred iris images demonstrate the code-level approach can achieve the highest accuracy in compared with the existing pixel-level, feature-level, and score-level solutions.