NDLI: Label propagation approach for predicting missing biographic labels in face-based biometric records

Content Provider	IET Digital Library
Author	Swearingen, Thomas Ross, Arun
Abstract	A biometric system uses the physical or behavioural attributes of a person, such as face, fingerprint, iris or voice, to recognise an individual. Many operational biometric systems store the biographic information of an individual, viz., name, gender, age and ethnicity, besides the biometric data itself. Thus, the biometric record pertaining to an individual consists of both biometric data and biographic data. We propose the use of a graph structure to model the relationship between the biometric records in a database. We show the benefits of such a graph in deducing biographic labels of incomplete records, i.e. records that may have missing biographic information. In particular, we use a label propagation scheme to deduce missing values for both binary-valued biographic attributes (e.g. gender) as well as multi-valued biographic attributes (e.g. age group). Experimental results using face-based biometric records consisting of name, age, gender and ethnicity convey the pros and cons of the proposed method.
Starting Page	71
Ending Page	80
Page Count	10
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 1, Jan (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2017.0117
Journal	IET Biometrics
Publisher Date	2017-09-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Behavioural Attributes Biographic Information Biomimetics Combinatorial Mathematics Computer Vision And Image Processing Technique Face Based Biometric Records Face Recognition Graph Theory Image Recognition Label Propagation Approach Operational Biometric System Physical Attributes Predicting Missing Biographic Labels Records Management Spatial And Pictorial Database Visual Database
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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