Toward Accurate and Fast Iris Segmentation for Iris Biometrics

@article{He2009TowardAA,
  title={Toward Accurate and Fast Iris Segmentation for Iris Biometrics},
  author={Zhaofeng He and Tieniu Tan and Zhenan Sun and Xianchao Qiu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2009},
  volume={31},
  pages={1670-1684}
}
  • Zhaofeng He, T. Tan, Xianchao Qiu
  • Published 1 September 2009
  • Computer Science
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first… 
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TLDR
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TLDR
Effective use of active shape models (ASMs) for doing iris segmentation is demonstrated and a method for building flexible model by learning patterns of iris invariability from a well organized training set is described.
Iris Localization via Pulling and Pushing
TLDR
This paper proposes a novel iris localization method based on a spring force-driven iteration scheme that is faster and more accurate than state-of-the-art iri localization methods.
Efficient iris recognition by characterizing key local variations
TLDR
The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris.
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TLDR
Experimental results on a set of 2,096 images show that the proposed method based on the fusion of edge and region information has encouraging performance for improving the recognition accuracy.
IRIS Segmentation: Detecting Pupil, Limbus and Eyelids
TLDR
An active contour model to accurately detect pupil boundary is presented to improve the performance of iris recognition systems and an iterative algorithm has been developed in order to capture limbus and eyelids.
The relative distance of key point based iris recognition
Iris recognition: an emerging biometric technology
TLDR
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.
Experiments with an improved iris segmentation algorithm
TLDR
An improved iris segmentation and eyelid detection stage of the algorithm is developed and implemented, which leads to an increase of over 6% in the rank-one recognition rate.
Boosting ordinal features for accurate and fast iris recognition
TLDR
A novel iris recognition method based on learned ordinal features, which outperforms Adaboost in terms of both accuracy and generalization capability across different iris databases and the well-known cascade architecture is adopted to reorganize the learned SOBoost classifier into a dasiacascadepsila.
Performance evaluation of non-ideal iris based recognition system implementing global ICA encoding
TLDR
A series of receiver operating characteristics (ROCs) demonstrates various effects on the performance of the non-ideal iris based recognition system implementing the global ICA encoding.
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