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Exploring Body Texture From mmW Images for Person Recognition
Universidad Autonoma de Madrid, Madrid, Spain.ORCID iD: 0000-0002-2428-3792
Universidad Autonoma de Madrid, Madrid, Spain.ORCID iD: 0000-0002-6338-8511
Universidad Autonoma de Madrid, Madrid, Spain.ORCID iD: 0000-0002-6343-5656
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
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2019 (English)In: IEEE Transactions on Biometrics, Behavior, and Identity Science, E-ISSN 2637-6407, Vol. 1, no 2, p. 139-151Article in journal (Refereed) Published
Abstract [en]

Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants, such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this paper we aim to gain some insight about the potential of using mmW texture information for the same task, considering not only the mmW face, but also mmW torso and mmW wholebody. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained convolutional neural networks (CNNs) models. First, we analyze the individual performance of three mmW body parts, concluding that: 1) mmW torso region is more discriminative than mmW face and the whole body; 2) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body; and 3) hand-crafted features slightly outperform CNN features on mmW torso. In the second part of this paper, we analyze different multi-algorithmic and multi-modal techniques, including a novel CNN-based fusion technique, improving verification results to 2% EER and identification rank-1 results up to 99%. Comparative analyses with mmW body shape information and face recognition in the visible and NIR spectral bands are also reported.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2019. Vol. 1, no 2, p. 139-151
Keywords [en]
mmW imaging, body texture information, border control security, hand-crafted features, deep learning features, CNN-level multimodal fusion, body parts
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-40622DOI: 10.1109/TBIOM.2019.2906367OAI: oai:DiVA.org:hh-40622DiVA, id: diva2:1353920
Projects
KK-CAISRKK-SIDUS AIR
Part of project
Ocular biometrics in unconstrained sensing environments, Swedish Research Council
Funder
Swedish Research CouncilKnowledge Foundation
Note

Funding: This work was supported in part by the Project CogniMetrics through MINECO/FEDER under Grant TEC2015-70627-R, and in part by the SPATEK Network under Grant TEC2015-68766-REDC. The work of E. Gonzalez-Sosa was supported by the Ph.D. Scholarship from 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. The work of V. M. Patel was supported in part by the U.S. Office of Naval Research under Grant YIP N00014-16-1-3134.

Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-09-25Bibliographically approved

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Alonso-Fernandez, Fernando

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Gonzalez-Sosa, EsterVera-Rodriguez, RubenFierrez, JulianAlonso-Fernandez, FernandoPatel, Vishal M.
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