Impact of (segmentation) quality on long vs. short-timespan assessments in iris recognition performance

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.