A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accommodation in computer vision
Accommodation in computer vision
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Genetic Algorithm Based Parameter Identification of Defocused Image
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
Localized iris image quality using 2-d wavelets
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Robust and fast assessment of iris image quality
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A study on iris image restoration
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
IEEE Transactions on Circuits and Systems for Video Technology
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During video acquisition of an automatic non-cooperative biometric iris recognition system, not all the iris images obtained from the video sequence are suitable for recognition. Hence, it is important to acquire high quality iris images and quickly identify them in order to eliminate the poor quality ones (mostly defocused images) before the subsequent processing. In this paper, we present the results of a comparative analysis of four methods for iris image quality assessment to select clear images in the video sequence. The goal is to provide a solid analytic ground to underscore the strengths and weaknesses of the most widely implemented methods for iris image quality assessment. The methods are compared based on their robustness to different types of iris images and the computational effort they require. The experiments with the built database (100 videos from MBGC v2) demonstrate that the best performance scores are generated by the kernel proposed by Kang & Park. The FAR and FRR obtained are 1.6% and 2.3% respectively.