Open Access
Recognition of Human Iris Patterns for Biometric Identification
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TLDR
The work presented in this thesis involved developing an ‘open-source’ iris recognition system in order to verify both the uniqueness of the human iris and also its performance as a biometric.Abstract:
A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favourable conditions, and there have been no independent trials of the technology. The work presented in this thesis involved developing an ‘open-source’ iris recognition system in order to verify both the uniqueness of the human iris and also its performance as a biometric. For determining the recognition performance of the system two databases of digitised greyscale eye images were used. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localise the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalised into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantised to four levels to encode the unique pattern of the iris into a bit-wise biometric template. The Hamming distance was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed. The system performed with perfect recognition on a set of 75 eye images; however, tests on another set of 624 images resulted in false accept and false reject rates of 0.005% and 0.238% respectively. Therefore, iris recognition is shown to be a reliable and accurate biometric technology.read more
Citations
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Proceedings Article
Iris recognition: an emerging biometric technology
Sanjay Ganorkar,Ashok A. Ghatol +1 more
TL;DR: Iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described and experimental results show that the proposed method has an encouraging performance.
Journal ArticleDOI
Image understanding for iris biometrics: A survey
TL;DR: This survey covers the historical development and current state of the art in image understanding for iris biometrics and suggests a short list of recommended readings for someone new to the field to quickly grasp the big picture of irisBiometrics.
Book ChapterDOI
UBIRIS: a noisy iris image database
Hugo Proença,Luís A. Alexandre +1 more
TL;DR: A new iris database that contains images with noise is presented, in contrast with the existing databases, that are noise free.
Journal ArticleDOI
Comparison and combination of iris matchers for reliable personal authentication
Ajay Kumar,Arun Passi +1 more
TL;DR: It is suggested that the performance from the Haar wavelet and Log-Gabor filter based phase encoding is the most promising among all the four approaches considered in this work and the combination of these two matchers is most promising, both in terms of performance and the computational complexity.
Journal ArticleDOI
Iris Segmentation Using Geodesic Active Contours
Samir Shah,Arun Ross +1 more
TL;DR: This paper describes a novel iris segmentation scheme employing geodesic active contours (GACs) to extract the iris from the surrounding structures and demonstrates the efficacy of the proposed technique on the CASIA v3.0 and WVU nonideal iris databases.
References
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TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
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Proceedings ArticleDOI
How iris recognition works
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.