Paper Info
Title
Adaptive fusion of biometric and biographic information for identity de-duplication.
Abstract
An efficient, robust and accurate de-duplication mechanism.Real-time prediction whether additional identifiers are needed to make a decision.Biographic information is utilized for only a small fraction of queries. Use of biometrics for person identification has increased tremendously over the past decade, e.g., in large scale national identification programs, for law enforcement and border control applications, and social welfare initiatives. For such large scale applications with a diverse target population, unimodal biometric systems, which use a single biometric trait (e.g., fingerprints), are inadequate due to their limited capacity. Multimodal biometric systems, which fuse multiple biometric traits (e.g., fingerprints and face), are required for large-scale identification applications, e.g., de-duplication where the goal is to ensure that the same person does not have two different official credentials (e.g., national ID card) based on different credentials. While multimodal biometric systems offer several advantages (e.g., improvement in recognition accuracy, decrease in failure to enroll rate), they require large enrollment and de-duplication times. This paper proposes an adaptive sequential framework to automatically determine which subset of biometric traits and biographic information is adequate for de-duplication of a given query. An analysis of this strategy is presented on a virtual multi-biometric database of 27,000 subjects (fingerprints from NIST SD14 dataset and face images from the PCSO dataset) along with biographic information sampled from the US census data. Experimental results, using three-fold cross-validation, show that without any loss in de-duplication accuracy, on average, for 63.18% (of a total of 27,000) of the queries, only fingerprint capture is adequate, for an additional 28.69% of queries, both fingerprint and face are required, and only 8.13% of the queries needed biographic information in addition to fingerprint and face. Display Omitted
Year
DOI
Venue
2016
10.1016/j.patrec.2016.10.011
Pattern Recognition Letters
Keywords
Field
DocType
Identity de-duplication,Biometric traits,Biographic information,Adaptive fusion,Sequential methods
Data deduplication,Computer vision,Population,Pattern recognition,Identifier,Computer science,Sequential method,Fingerprint,NIST,Artificial intelligence,Biometrics,Law enforcement
Journal
Volume
Issue
ISSN
84
C
0167-8655
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Prem Sewak Sudhish1102.69
Anil Jain2335073334.84
Kai Cao320718.68