@inproceedings{df476695c8c045beb791b25a621ede38,
title = "Iris image evaluation for non-cooperative biometric iris recognition system",
abstract = "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.",
keywords = "Convolution, Defocus, Iris recognition, Kernel, MBGC, Quality, Video",
author = "Colores, {Juan M.} and Mireya Garc{\'i}a-V{\'a}zquez and Alejandro Ram{\'i}rez-Acosta and H{\'e}ctor P{\'e}rez-Meana",
year = "2011",
doi = "10.1007/978-3-642-25330-0_44",
language = "Ingl{\'e}s",
isbn = "9783642253294",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "499--509",
booktitle = "Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings",
edition = "PART 2",
note = "10th Mexican International Conference on Artificial Intelligence, MICAI 2011 ; Conference date: 26-11-2011 Through 04-12-2011",
}