Proceedings Article10.1109/NGNS.2011.6142547
Study the iris segmentation method for biometric pattern recognition
Hentati Raida,Moncef Boussellmi,Mohamed Abid +2 more
- 01 Dec 2011
- pp 108-111
2
TL;DR: A new biometric-based Iris segmentation system that acquires the biometric data in the data base CASIA version 1.0 and the experimental results are compared to release the performances of the algorithms.
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Abstract: Several system require authenticating a person's identity before giving access to resources. With new advances in technology, biometrics is one of the most promising techniques in recognition human. Biometrics intends to identify a person by his physical and/or behavioural characteristic. Among biometric technologies, iris recognition has received increasing attention due to its high reliability. Iris recognition is a biometric authentication technology that uses pattern recognition techniques based on iris characteristics. In this paper we propose a new biometric-based Iris segmentation system. The system acquires the biometric data in the data base CASIA version 1.0. The experimental results are compared to release the performances of our algorithms.
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Citations
A low complexity Iris localization algorithm for Iris biometrics
Shahrukh Agha,Farmanullah Jan +1 more
TL;DR: A low-complexity iris localization algorithm that takes less than a second to mark both iris contours, which is a green signal for its real-time applications.
2
A Fast and Accurate Iris Localization Technique for Healthcare Security System
Alaa S. Al-Waisy,Rami Qahwaji,Stanley S. Ipson,Shumoos Al-Fahdawi +3 more
- 01 Oct 2015
TL;DR: An efficient and automatic method is presented for the inner and outer iris boundary localization and a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries.
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TL;DR: In this article, the sign of the projection of many different parts of the iris onto these filters determines each bit in an iris code, and the similarity metric (Hamming distance) is computed from the XOR of any two iris codes.
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