Paper Info
Title
Cross-Age Face Recognition on a Very Large Database: The Performance versus Age Intervals and Improvement Using Soft Biometric Traits
Abstract
Facial aging can degrade the face recognition performance dramatically. Traditional face recognition studies focus on dealing with pose, illumination, and expression (PIE) changes. Considering a large span of age difference, the influence of facial aging could be very significant compared to the PIE variations. How big the aging influence could be? What is the relation between recognition accuracy and age intervals? Can soft biometrics be used to improve the face recognition performance under age variations? In this paper we address all these issues. First, we investigate the face recognition performance degradation with respect to age intervals between the probe and gallery images on a very large database which contains about 55,000 face images of more than 13,000 individuals. Second, we study if soft biometric traits, e.g., race, gender, height, and weight, could be used to improve the cross-age face recognition accuracies, and how useful each of them could be.
Year
DOI
Venue
2010
10.1109/ICPR.2010.828
ICPR
Keywords
Field
DocType
face recognition performance,face image,cross-age face recognition,recognition accuracy,facial aging,age interval,age variation,age intervals,soft biometric traits,age difference,traditional face recognition study,face recognition performance degradation,large database,cross-age face recognition accuracy,very large database,accuracy,principal component analysis,face recognition,face,aging,databases
Computer vision,Facial recognition system,Soft biometrics,Biometrics access control,Three-dimensional face recognition,Pattern recognition,Computer science,Very large database,Speech recognition,Artificial intelligence,Biometrics,Principal component analysis
Conference
Citations 
PageRank 
References 
23
0.75
10
Authors
3
Name
Order
Citations
PageRank
Guodong Guo12548144.00
Guowang Mu256216.22
Karl Ricanek316518.65