Paper Info
Title
On the effectiveness of soft biometrics for increasing face verification rates
Abstract
The term soft biometrics typically refers to attributes of people such as their gender, the shape of their head or the color of their hair. There is growing interest in soft biometrics as a means of improving automated face recognition since they hold the promise of significantly reducing recognition errors, in part by ruling out illogical choices. This paper concentrates specifically on soft biometrics as opposed to extended attributes, and presents the results from three experiments quantifying performance gains on a difficult face recognition task when standard face recognition algorithms are augmented using soft biometrics. These experiments include (1) a best-case analysis using perfect knowledge of gender and race, (2) support vector machine-based soft biometric classifiers and (3) face shape expressed through an active shape model. All three experiments indicate small improvements may be made when soft biometrics augment an existing algorithm. However, in all cases, the gains were modest. One reason is that false matches are more likely between faces of people sharing the same soft biometric traits. This is to be expected, since face recognition algorithms utilize appearance information, which is the same information used by algorithms designed to assign soft biometric labels to face images.
Year
DOI
Venue
2015
10.1016/j.cviu.2015.03.003
Computer Vision and Image Understanding
Keywords
Field
DocType
Soft biometrics,Face recognition,Evaluation
Face verification,Computer vision,Active shape model,Facial recognition system,Soft biometrics,Three-dimensional face recognition,Support vector machine,Speech recognition,Artificial intelligence,Biometrics,Face detection,Mathematics
Journal
Volume
Issue
ISSN
137
C
1077-3142
Citations 
PageRank 
References 
12
0.50
36
Authors
4
Name
Order
Citations
PageRank
Hao Zhang11034.63
J. Ross Beveridge21716190.52
Bruce A. Draper32001207.57
P. Jonathon Phillips49209801.62