Biometric systems compare biometric samples to produce matching scores. However, the corresponding distributions are often heterogeneous and as a result it is hard to specify a threshold that works well in all cases. Score normalization techniques exploit the score distributions to improve the recognition performance. The goals of this chapter are to (i) introduce the reader to the concept of score normalization and (ii) answer important questions such as why normalizing matching scores is an effective and efficient way of exploiting score distributions, and when such methods are expected to work. In particular, the first section highlights the importance of normalizing matching scores; offers intuitive examples to demonstrate how variations between different (i) biometric samples, (ii) modalities, and (iii) subjects degrade recognition performance; and answers the question of why score normalization effectively utilizes score distributions. The next three sections offer a review of score normalization methods developed to address each type of variation. The chapter concludes with a discussion of why such methods have not gained popularity in the research community and answers the question of when and how one should use score normalization.
|Original language||English (US)|
|Title of host publication||Face Recognition Across the Imaging Spectrum|
|Publisher||Springer International Publishing|
|Number of pages||21|
|State||Published - Jan 1 2016|
ASJC Scopus subject areas
- Computer Science(all)