کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4970309 | 1365309 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
The iris segmentation module plays a crucial role in iris recognition system as it allows to define the exact iris texture region in the image of the eye. Usual iris segmentation methods tend to fail on challenging eye images captured in less constrained environment or at-a-distance. In this paper, we propose a new robust model to segment degraded iris images. Its main characteristics are as follows: (1) we explore the use of advanced statistical model for unsupervised iris segmentation and more particularly, we focused on Hidden Markov Chain. (2) Novel adequate image scanning procedure and initialization step for implementing this model are developed. (3) The implementation of the proposed model can be performed on reduced image resolutions allowing limiting the processing time without degradation of the performance. A novel recognition system can therefore be obtained by adding this unsupervised iris segmentation module as a preprocessing in the open-source recognition model OSIRIS-V4. Extensive experiments on two large near infra-red databases ICE2005 and CASIA-IrisV4-distance demonstrate a significant improvement of the recognition performance with this novel system compared to OSIRIS-V4 and recent region-based iris verification systems, showing this way the potential of such statistical models for iris recognition.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 82, Part 2, 15 October 2016, Pages 116-123
Journal: Pattern Recognition Letters - Volume 82, Part 2, 15 October 2016, Pages 116-123
نویسندگان
Meriem Yahiaoui, Emmanuel Monfrini, Bernadette Dorizzi,