دورية أكاديمية
Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping.
| العنوان: | Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping. |
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| المؤلفون: | Rubio A; Department of Computer Science & Artificial Intelligence, IMT Mines Ales, Ales, France., Magnier B; EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France.; Service de Médecine Nucléaire, Centre Hospitalier Universitaire de Nîmes, Université de Montpellier, Nîmes, France. |
| المصدر: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Jul 24; Vol. 24 (15). Date of Electronic Publication: 2024 Jul 24. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Basel, Switzerland : MDPI, c2000- |
| مواضيع طبية MeSH: | Iris*/diagnostic imaging , Algorithms* , Biometric Identification*/methods , Image Processing, Computer-Assisted*/methods, Humans ; Biometry/methods ; Databases, Factual ; Machine Learning |
| مستخلص: | This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved 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, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios. |
| References: | IEEE Trans Syst Man Cybern B Cybern. 2007 Oct;37(5):1167-75. (PMID: 17926700) IEEE Trans Pattern Anal Mach Intell. 1986 Jun;8(6):679-98. (PMID: 21869365) |
| فهرسة مساهمة: | Keywords: Canny algorithm; Hough transform; binarized statistical image features; computer vision; image preprocessing; iris biometric recognition; iris unwrapping |
| تواريخ الأحداث: | Date Created: 20240810 Date Completed: 20240810 Latest Revision: 20240812 |
| رمز التحديث: | 20240813 |
| مُعرف محوري في PubMed: | PMC11315010 |
| DOI: | 10.3390/s24154805 |
| PMID: | 39123851 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1424-8220 |
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| DOI: | 10.3390/s24154805 |