Version 1
: Received: 17 June 2024 / Approved: 18 June 2024 / Online: 18 June 2024 (11:20:53 CEST)
How to cite:
Rubio, A.; Magnier, B. Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping. Preprints2024, 2024061218. https://doi.org/10.20944/preprints202406.1218.v1
Rubio, A.; Magnier, B. Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping. Preprints 2024, 2024061218. https://doi.org/10.20944/preprints202406.1218.v1
Rubio, A.; Magnier, B. Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping. Preprints2024, 2024061218. https://doi.org/10.20944/preprints202406.1218.v1
APA Style
Rubio, A., & Magnier, B. (2024). Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping. Preprints. https://doi.org/10.20944/preprints202406.1218.v1
Chicago/Turabian Style
Rubio, A. and Baptiste Magnier. 2024 "Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping" Preprints. https://doi.org/10.20944/preprints202406.1218.v1
Abstract
This work investigates into the enhancement of iris recognition systems through a two-module approach focusing on low level image preprocessing techniques and advanced feature extraction. The first module is dedicated to the preprocessing of iris images, leveraging the Canny algorithm for edge detection followed by the circle-based Hough transform for precise iris extraction. This process, integral to our methodology, ensures the quality and consistency necessary for the subsequent Binary Statistical Image Features (BSIF) analysis. The second module employs the BSIF technique, incorporating domain-specific filters trained on iris-specific data, for robust biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset composed of 1892 iris images demonstrate the effectiveness of the developped approach, with potential future improvements including adaptive algorithms and machine learning integrations for enhanced performance in diverse and unpredictable real-world applications. The novel contributions of this paper lie in the provision of a reproducible research framework, making the iris-domain-specific BSIF filters, training patches, testing database, and source codes publicly available for further application and study.
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
