TY - GEN
T1 - Measurement of defocus level in iris images using different convolution kernel methods
AU - Colores-Vargas, J. Miguel
AU - García-Vázquez, Mireya S.
AU - Ramírez-Acosta, Alejandro A.
PY - 2010
Y1 - 2010
N2 - During the video and fixed image acquisition procedure of an automatic iris recognition system, it is essential to acquire focused iris images. If defocus iris images are acquired, the performance of the iris recognition is degraded, because iris images don't have enough feature information. Therefore it's important to adopt the image quality evaluation method before the image processing. In this paper, it is analyzed and compared four representative quality assessment methods on the MBGC iris database. Through methods, it can fast grade the images and pick out the high quality iris images from the video sequence captured by real-time iris recognition camera. The experimental results of the four methods according to the receiver operating characteristic (ROC) curve are shown. Then the optimal method of quality evaluation that allows better performance in an automatic iris recognition system is founded. This paper also presents an analysis in terms of computation speed of the four methods.
AB - During the video and fixed image acquisition procedure of an automatic iris recognition system, it is essential to acquire focused iris images. If defocus iris images are acquired, the performance of the iris recognition is degraded, because iris images don't have enough feature information. Therefore it's important to adopt the image quality evaluation method before the image processing. In this paper, it is analyzed and compared four representative quality assessment methods on the MBGC iris database. Through methods, it can fast grade the images and pick out the high quality iris images from the video sequence captured by real-time iris recognition camera. The experimental results of the four methods according to the receiver operating characteristic (ROC) curve are shown. Then the optimal method of quality evaluation that allows better performance in an automatic iris recognition system is founded. This paper also presents an analysis in terms of computation speed of the four methods.
KW - Convolution kernel
KW - defocus
KW - iris
KW - quality
KW - video
UR - http://www.scopus.com/inward/record.url?scp=78751506572&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15992-3_14
DO - 10.1007/978-3-642-15992-3_14
M3 - Contribución a la conferencia
SN - 3642159915
SN - 9783642159916
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 125
EP - 133
BT - Advances in Pattern Recognition - Second Mexican Conference on Pattern Recognition, MCPR 2010, Proceedings
T2 - Mexican Conference on Pattern Recognition 2010, MCPR 2010
Y2 - 27 September 2010 through 29 September 2010
ER -