%0 Electronic Article %A Peter Wild %+ Computational Vision Group, School of Systems Engineering, University of Reading, Reading RG6 6AY, UK %A James Ferryman %+ Computational Vision Group, School of Systems Engineering, University of Reading, Reading RG6 6AY, UK %A Andreas Uhl %+ Multimedia Signal Processing and Security Laboratory, Department of Computer Sciences, University of Salzburg, Salzburg 5020, Austria %K quality assessment %K long-timespan image database %K segmentation quality %K public datasets %K short-timespan assessments %K transparent recording setup %K ageing impact %K feature extraction %K temporal effects %K metadata %K segmentation module %K iris recognition performance %K iris recognition accuracy %K performance-impacting factors %X Several researchers have presented studies of temporal effects on iris recognition accuracy, with varying results on severity of observed effects. The sensitive topic continues to be adversely discussed and the difficulty of isolating performance-impacting factors is immanent. The impact of ageing on segmentation vs. feature extraction has been largely neglected so far. This study attempts to shed light on the impact of segmentation quality on observed temporal effects highlighting the critical role of the segmentation module and quality assessment when assessing ageing effects. The lack of large and standardised temporal variation in public datasets as well as additional metadata (age of subject, recording parameters) and strictly enforced unified recording and quality guidelines over time-separated sessions are identified as imminent problems of ageing studies. Results are reported on a long-timespan database of 36,240 images comprising 104 classes and a 4 year time lapse, offering a large variety of recording conditions highlighting the critical role of a transparent recording setup. %@ 2047-4938 %T Impact of (segmentation) quality on long vs. short-timespan assessments in iris recognition performance %B IET Biometrics %D December 2015 %V 4 %N 4 %P 227-235 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=i44dmsuhd5u9.x-iet-live-01content/journals/10.1049/iet-bmt.2014.0073 %G EN