Abstract
In recent years, periocular recognition has become a popular alternative to face and iris recognition in less ideal acquisition scenarios. An interesting example of such scenarios is the usage of mobile devices for recognition purposes. With the growing popularity and easy access to such devices, the development of robust biometric recognition algorithms to work under such conditions finds strong motivation. In the present work we assess the performance of extended versions of two state-of-the-art periocular recognition algorithms on the publicly available CSIP database, a recent dataset composed of images acquired under highly unconstrained and multi-sensor mobile scenarios. The achieved results show each algorithm is better fit to tackle different scenarios and applications of the biometric recognition problem.
Keywords
- Periocular Recognition
- Biometric Recognition Problem
- Multi-layer Perceptron Artificial Neural Network
- Periocular Region
- Individual-specific Models (IDSM)
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgments
The first author would like to thank Fundação para a Ciência e Tecnologia (FCT) - Portugal the financial support for the PhD grant SFRH/BD/87392/2012. The second and fifth authors would like to acknowledge the financial support obtained from North Portugal Regional Operational Programme (ON.2 - O Novo Norte), Portuguese National Strategic Reference Framework (NSRF) and the European Regional Development Fund (EDRF) from European Union through project ICT4DCC (NORTE-07-0124-FEDER-000042.). The third and fourth authors would like to acknowledge the financial support provided by FCT through the research grant SFRH/BD/80182/2011, and the RSU - Remote Sensing Unit through PEst-OE-FIS/UI0524/2014.
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Monteiro, J.C., Esteves, R., Santos, G., Fiadeiro, P.T., Lobo, J., Cardoso, J.S. (2015). A Comparative Analysis of Two Approaches to Periocular Recognition in Mobile Scenarios. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_25
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DOI: https://doi.org/10.1007/978-3-319-27863-6_25
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