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Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and Evaluation
Nokia Bell-Labs, Madrid, Spain & Universidad Autonoma de Madrid, Madrid, Spain.ORCID iD: 0000-0002-2428-3792
Universidad Autonoma de Madrid, Madrid, Spain.ORCID iD: 0000-0002-6343-5656
Universidad Autonoma de Madrid, Madrid, Spain.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-1400-346X
2018 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 13, no 8, p. 2001-2014Article in journal (Refereed) Published
Abstract [en]

The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems (COTS). All experiments are reported using the labeled faces in the wild (LFW) database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores. © 2018 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 13, no 8, p. 2001-2014
Keywords [en]
Soft biometrics, hard biometrics, commercial systems, unconstrained scenarios
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-36651DOI: 10.1109/TIFS.2018.2807791ISI: 000429228800010Scopus ID: 2-s2.0-85039786512OAI: oai:DiVA.org:hh-36651DiVA, id: diva2:1199505
Projects
SIDUS-AIR
Funder
Swedish Research CouncilKnowledge Foundation
Note

Funded in part by the Spanish Guardia Civil and the project CogniMetrics from MINECO/FEDER under Grant TEC2015-70627-R and in part by the Imperial College London under Grant PRX16/00580. The work of E. Gonzalez-Sosa was supported by a Ph.D. Scholarship from the Universidad Autonoma de Madrid. The work of F. Alonso-Fernandez was supported in part by the Swedish Research Council, in part by the CAISR program, and in part by the SIDUS-AIR project of the Swedish Knowledge Foundation. 

Available from: 2018-04-20 Created: 2018-04-20 Last updated: 2018-04-23Bibliographically approved

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Alonso-Fernandez, Fernando

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Gonzalez-Sosa, EsterFierrez, JulianAlonso-Fernandez, Fernando
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