S.S. Behera
Cross-spectral Periocular Recognition: a Survey
Behera, S.S.; Mandal, Bappaditya; Puhan, N.B.
Abstract
Among many biometrics such as face, iris, fingerprint and others, periocular region has the advantages over other biometrics because it is non-intrusive and serves as a balance between iris or eye region (very stringent, small area) and the whole face region (very relaxed large area). Research have shown that this is the region which does not get affected much because of various poses, aging, expression, facial changes and other artifacts, which otherwise would change to a large variation. This region can be captured using the similar setups used for obtaining face and iris images. Active research has been carried out on this topic since past few years due to its obvious advantages over face and iris biometrics in unconstrained and uncooperative scenarios. Many researchers have explored periocular biometrics involving both visible (VIS) and infra-red (IR) spectrum images. For a system to work for 24/7 (such as in surveillance scenarios), the registration process may depend on the day time VIS periocular images (or any mug shot image) and the testing or recognition process may occur in the night time involving only IR periocular images. This gives rise to a challenging research problem called the cross-spectral matching of images where VIS images are used for registration or as gallery images and IR images are used for testing or recognition process and vice versa. After intensive research of more than two decades on face and iris biometrics in crossspectral domain, a number of researchers have now focused their work on matching heterogeneous (cross-spectral) periocular images. Though a number of surveys have been made on existing periocular biometric research, no study has been done on its cross-spectral aspect. This paper analyses and reviews current state-of-the-art techniques in cross-spectral periocular recognition including various methodologies, databases, their protocols and current-state-of-the-art recognition performances.
Citation
Behera, S., Mandal, B., & Puhan, N. (2019). Cross-spectral Periocular Recognition: a Survey. In Emerging Research in Electronics, Computer Science and Technology (731–741). https://doi.org/10.1007/978-981-13-5802-9_64
Conference Name | 3rd International Conference on Emerging Research in Electronics, Computer Science and Technology - International Conference, ICERECT 2018 |
---|---|
Start Date | Aug 23, 2018 |
End Date | Aug 24, 2018 |
Acceptance Date | Aug 20, 2018 |
Online Publication Date | Apr 24, 2019 |
Publication Date | Apr 25, 2019 |
Publisher | Springer Verlag |
Volume | 545 |
Pages | 731–741 |
Series Title | Lecture Notes in Electrical Engineering |
Series ISSN | 1876-1100; 1876-1119 |
Book Title | Emerging Research in Electronics, Computer Science and Technology |
ISBN | 978-981-13-5801-2 |
DOI | https://doi.org/10.1007/978-981-13-5802-9_64 |
Keywords | periocular recognition, cross-spectral matching, infra-red |
Publisher URL | https://doi.org/10.1007/978-981-13-5802-9_64 |
Files
Cross_spectral_Periocular_Recognition__A_Survey19Aug2018.pdf
(1.3 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/
You might also like
Towards Quantification of Eye Contacts Between Trainee Doctors and Simulated Patients in Consultation Videos
(2024)
Conference Proceeding
Unified Deep Ensemble Architecture for Multiple Classification Tasks
(2024)
Conference Proceeding
Grid LSTM based Attention Modelling for Traffic Flow Prediction
(2024)
Conference Proceeding
Visual Attention Assisted Games
(2023)
Conference Proceeding
Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction
(2023)
Conference Proceeding
Downloadable Citations
About Keele Repository
Administrator e-mail: research.openaccess@keele.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search