Why rank-level fusion? And what is the impact of image quality?
Emanuela Marasco, Ayman A. Abaza, Bojan Čukić · International Journal of Big Data Intelligence · 2015
Recent research has established benefits of rank-level fusion in identification systems; however, these studies have not compared the advantages, if any, of rank-level fusion schemes over classical score-level fusion schemes. In the presence of low quality biometric data, the genuine match score is claimed to be low and expected to be an unreliable individual output. Conversely, the rank assigned to that genuine identity is believed to remain stable even when using low quality biometric data. However, to the best of our knowledge, there is not a deepen investigation on the stability of ranks. The contribution of this paper is two-fold: 1) investigating the rank stability in both unimodal and multimodal biometric systems; 2) comparing the identification performance of rank-level and score-level fusion in the presence of low quality data. The results show that a variant of the highest rank fusion scheme, performs better than other tested non-learning rank-level fusion methods.