An Efficient and Accurate Iris Segmentation Technique

Mahadeo, Nitin K.; Bhattacharjee, Nandita

doi:10.1109/dicta.2009.65

Cited by 7 publications

(11 citation statements)

References 16 publications

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“…In this section, we present the results of our experimental analysis and a comparison of the Daugman [2] and Masek/Wildes [15] segmentation methods with our proposed new method [4], and also with the range of selected segmentation methods described earlier [5][6][7][8][9][10][11][12]15]. Our comparative analysis is based on performance with respect to accuracy, and computation time.…”

Section: Resultsmentioning

confidence: 98%

“…Mahadeo and Bhattacharjee [5] make use of morphological operators and several image processing techniques to obtain a binary image and an initial pupil boundary. For iris boundary detection, firstly the edge image is obtained by using the Canny edge detector and then noise pixels are removed from the edge image.…”

Section: Iris Segmentation Methodsmentioning

confidence: 99%

“…Many new segmentation methods [5][6][7][8][9][10][11][12][13] have been published in recent years to address these factors and to improve the performance of iris segmentation for more robust iris recognition systems. In this paper, however, in order to be consistent across the reported experiments and comparisons, we investigate only segmentation algorithms for which performance has been reported based on the same sample database, in this case the CASIA V1.0 database [14].…”

Section: Iris Segmentation Methodsmentioning

confidence: 99%

“…For our comparative study, results reported in the more recently proposed methods investigated [5][6][7][8][9][10][11][12]15] are utilized.…”

Section: Experimental Infrastructurementioning

confidence: 99%

“…In this study, an experimental analysis is performed to test and compare the robustness, accuracy and computation time of the two earliest segmentation methods proposed by Daugman [2] and Wildes [3] with the more recently proposed methods introduced above [5][6][7][8][9][10][11][12]15] and with our proposed segmentation method [4].…”

Section: Experimental Infrastructurementioning

confidence: 99%

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Evaluating iris segmentation for scenario optimisation

Erbilek¹,

Fairhurst²

2011

4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011)

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Iris recognition is a biometric modality which offers the potential for high accuracy and, increasingly, for application in more diverse environments than hitherto. Poor segmentation is one of the most important factors likely to compromise iris recognition performance. Hence, research in the area of iris biometrics has often been focused on efforts to enhance the performance of iris segmentation techniques, and this has led to considerable work on iris segmentation. This paper presents a detailed investigation, evaluation and comparison of several segmentation approaches (including a new algorithm proposed by the authors) proposed in the literature based on their accuracy and processing speed. To be consistent with the research of others, for all quantitative experiments, algorithms have been evaluated on two iris databases, namely CASIA V1.0 and a subset of the BioSecure database.

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Section: Resultsmentioning

confidence: 98%

Section: Iris Segmentation Methodsmentioning

confidence: 99%

Section: Iris Segmentation Methodsmentioning

confidence: 99%

“…For our comparative study, results reported in the more recently proposed methods investigated [5][6][7][8][9][10][11][12]15] are utilized.…”

Section: Experimental Infrastructurementioning

confidence: 99%

Section: Experimental Infrastructurementioning

confidence: 99%

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Evaluating iris segmentation for scenario optimisation

Erbilek¹,

Fairhurst²

2011

4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011)

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Analysis of physical ageing effects in iris biometrics

Fairhurst

Erbilek

2011

IET Comput. Vis.

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Predicting segmentation errors in an iris recognition system

Mahadeo

Haffari

Paplinski

2015

2015 International Conference on Biometrics (ICB)

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Iris segmentation is defined as the isolation of the iris pattern in an eye image. A highly accurate segmented iris plays a key role in the overall performance of an iris recognition system, as shown in previous research. We present a fully automated method for classifying correctly and incorrectly segmented iris regions in eye images. In contrast with previous work where only iris boundary detection is considered (using a limited number of features), we introduce the following novelties which greatly enhance the performance of an iris recognition system. Firstly, we go beyond iris boundary detection and consider a more realistic and challenging task of complete segmentation which includes iris boundary detection and occlusion detection (due to eyelids and eyelashes). Secondly, an extended and rich feature set is investigated for this task. Thirdly, several non-linear learning algorithms are used to measure the prediction accuracy. Finally, we extend our model to iris videos, taking into account neighbouring frames for a better prediction. Both intrinsic and extrinsic evaluation are carried out to evaluate the performance of the proposed method. With these innovations, our method outperforms current state-ofthe-art techniques and presents a reliable approach to the task of classifying segmented iris images in an iris recognition system.

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An Efficient and Accurate Iris Segmentation Technique

Cited by 7 publications

References 16 publications

Evaluating iris segmentation for scenario optimisation

Evaluating iris segmentation for scenario optimisation

Analysis of physical ageing effects in iris biometrics

Predicting segmentation errors in an iris recognition system

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