Paper Info
Title
A Pruning Approach Improving Face Identification Systems
Abstract
We propose, in this paper, a new biometric identification approach which aims to improve recognition performances in identification systems. We aim to split the identity database into well separated partitions in order to simplify the identification task. In this paper we develop a face identification system and we use the reference algorithms of Eigenfaces and Fisherfaces in order to extract different features describing each identity. These features, which describe faces, are generally optimized to establish the required identity in a classical identification process. In this work, we develop a novel criterion to extract features used to partition the identity database. We develop database partitioning with clustering methods which split the gallery by bringing together identities which have similar features and separating dissimilar features in different bins. Pruning the most dissimilar bins from the query identity features allows us to improve the identification performances. We report results from the XM2VTS database.
Year
DOI
Venue
2009
10.1109/AVSS.2009.80
Genova
Keywords
Field
DocType
required identity,identification task,face identification systems,new biometric identification approach,identification system,identity database,pruning approach improving,identification performance,query identity feature,face identification system,xm2vts database,classical identification process,databases,face,data mining,biometry,feature extraction,clustering
Data mining,Biometrics access control,Eigenface,Pattern recognition,Computer science,Identification system,Feature extraction,Artificial intelligence,Biometrics,Image database,Cluster analysis,Pruning
Conference
ISBN
Citations 
PageRank 
978-0-7695-3718-4
2
0.43
References 
Authors
12
3
Name
Order
Citations
PageRank
Anis Chaari151.21
Sylvie Lelandais2299.96
Mohamed Ben Ahmed319545.34