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Title [sv]
Okulär biometrik i naturliga miljöer
Title [en]
Ocular biometrics in unconstrained sensing environments
Abstract [en]
The project is concerned with ocular biometrics in unconstrained sensing environments. Attention will be paid to the periocular modality (the part of the face surrounding the eye), which has shown a surprisingly high discrimination ability, and is the facial/ocular modality requiring the least constrained acquisition.One goal is to contribute with methods for efficient ocular detection and segmentation. This is still a challenge, with most works relying on manual image annotation, or on detecting the full face, which may not be reliable for example under occlusion. We will continue initiated work with symmetry filters, and will explore deep learning algorithms too, which are giving promising results in many computer vision tasks. Low resolution is another limitation. Thus, another goal will be super-resolution (SR) reconstruction of ocular images. With few works focused on iris, and none on periocular, adaptation of the many available SR methods to the particularities of ocular images is a promising avenue yet to be explored.Ubiquitous biometrics has emerged as critical not only in light of current security threats (e.g. identifying terrorists in surveillance videos), but also due to the proliferation of consumer electronics (e.g. smartphones) in need of continuous personal authentication for a wide variety of applications. By our contributions, we expect to be able to handle a wide range of variations in biometric imaging from these scenarios.
Publications (1 of 1) Show all publications
Gonzalez-Sosa, E., Vera-Rodriguez, R., Fierrez, J., Alonso-Fernandez, F. & Patel, V. M. (2019). Exploring Body Texture From mmW Images for Person Recognition. IEEE Transactions on Biometrics, Behavior, and Identity Science, 1(2), 139-151
Open this publication in new window or tab >>Exploring Body Texture From mmW Images for Person Recognition
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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
mmW imaging, body texture information, border control security, hand-crafted features, deep learning features, CNN-level multimodal fusion, body parts
National Category
Signal Processing
urn:nbn:se:hh:diva-40622 (URN)10.1109/TBIOM.2019.2906367 (DOI)
Swedish Research CouncilKnowledge Foundation

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
Principal InvestigatorAlonso-Fernandez, Fernando
Coordinating organisation
Halmstad University
2017-01-01 - 2020-12-31
National Category
Signal ProcessingComputer SystemsEmbedded Systems
DiVA, id: project:215Project, id: 2016-03497_VR

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