Paper Info
Title
Score Normalization in Multimodal Systems using Generalized Extreme Value Distribution.
Abstract
In multimodal biometric systems, human identification is performed by fusing information in different ways like sensor-level, feature-level, score-level, rank-level and decision-level. Score-level fusion is preferred over other levels of fusion because of its low complexity and sufficient availability of information for fusion. However, the scores obtained from different unimodal systems are heterogeneous in nature and hence they all require normalization before fusion. In this paper, we propose a clientcentric score normalization technique based on extreme value theory (EVT), exploiting the properties of Generalized Extreme Value (GEV) distribution. The novelty lies in the application of extreme value theory over the tail of the complete score distribution (genuine and impostor scores), assuming that the genuine scores form extreme values (tail) with respect to the entire set of scores. Normalization is then performed by estimating the cumulative density function of GEV distribution, using the parameter set obtained from genuine data. For evaluation, the proposed method is compared with state-of-the-art methods on two publicly available multimodal databases: i) NIST BSSR1 [22] multimodal biometric score database and ii) Database created from Face Recognition Grand Challenge V2.0 [23] and LG4000 iris images [24], to show the efficiency of the proposed method.
Year
Venue
Field
2015
BMVC
Normalization (statistics),Generalized extreme value distribution,Pattern recognition,Computer science,Extreme value theory,Cumulative distribution function,Face Recognition Grand Challenge,NIST,Artificial intelligence,Novelty,Biometrics
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
18
3
Name
Order
Citations
PageRank
Renu Sharma102.37
Sukhendu Das223833.82
Padmaja Joshi311.98