Estimating and Fusing Quality Factors for Iris Biometric Images

Kalka, Nathan D.; Zuo, Jinyu; Schmid, Natalia A.; Čukić, Bojan

doi:10.1109/tsmca.2010.2041658

Cited by 94 publications

(55 citation statements)

References 26 publications

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“…These methods are still being developed, achieving even better results for biometric purposes. In addition, several other form of biometric data are being studied, such as iris [3,[5][6][7], retina [8], palm, and ear images [9,10], which are seen as fairly stable data sources, even though aging may change the subject in the long run. Pupil sizes are used for human identification and verification [11].…”

Section: Introductionmentioning

confidence: 99%

On Biometrics With Eye Movements

Zhang

Juhola

2017

IEEE J. Biomed. Health Inform.

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Eye movements are a relatively novel data source for biometric identification. When video cameras applied to eye tracking become smaller and more efficient, this data source could offer interesting opportunities for the development of eye movement biometrics. In the present article, we study primarily biometric identification as seen as a classification task of multiple classes, and secondarily biometric verification considered as binary classification. Our research is based on the saccadic eye movement signal measurements from 109 young subjects. In order to te s t the dat a m e as ur e d, w e us e a pr o c e dur e o f biom e tri c identification according to the one-versus-one (subject) principle. In a development from our previous research, which also involved biometric verification based on saccadic eye movements, we now apply another eye movement tracker device with a higher sampling frequency of 250 Hz. The results obtained are good, with correct identification rates at 80-90% at their best.

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Section: Introductionmentioning

confidence: 99%

On Biometrics With Eye Movements

Zhang

Juhola

2017

IEEE J. Biomed. Health Inform.

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“…In this context, iris image quality assessment is an important trend in the field [17,12,5]. Several quality factors can degrade iris images [12].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, iris image quality assessment is an important trend in the field [17,12,5]. Several quality factors can degrade iris images [12]. However, evaluation in the segmentation performance is quite limited [21], with most of the works focused on its impact in the recognition accuracy [12,17].…”

Section: Introductionmentioning

confidence: 99%

Quality factors affecting iris segmentation and matching

Alonso‐Fernandez

Bigün

2013

2013 International Conference on Biometrics (ICB)

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“…For this reason, systems that are more robust and with higher reliability are needed to avoid the problems mentioned above. Pattern recognition systems based on neural networks have been given recently considerable interest [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

et al. 2012

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Abstrak Kata kunci: algoritma genetik, biometrik iris, fuzi, jaringan syaraf tiruan, optimasi Abstract This paper describes the application of modular neural network architectures for person recognition using the human iris image as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA). We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with

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Estimating and Fusing Quality Factors for Iris Biometric Images

Cited by 94 publications

References 26 publications

On Biometrics With Eye Movements

On Biometrics With Eye Movements

Quality factors affecting iris segmentation and matching

Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

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