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Exploring Deep Lear...
Exploring Deep Learning Image Super-Resolution for Iris Recognition
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- Ribeiro, Eduardo (author)
- University of Salzburg, Department of Computer Sciences, Salzburg, Austria & Federal University of Tocantins, Department of Computer Sciences, Tocantins, Brazil
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- Uhl, Andreas (author)
- University of Salzburg, Department of Computer Sciences, Salzburg, Austria
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- Alonso-Fernandez, Fernando, 1978- (author)
- Högskolan i Halmstad,Halmstad Embedded and Intelligent Systems Research (EIS),CAISR Centrum för tillämpade intelligenta system (IS-lab)
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- Farrugia, Reuben A. (author)
- University of Malta, Department of CCE, Msida, Malta
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2017
- 2017
- English.
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In: 25th European Signal Processing Conference (EUSIPCO 2017). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780992862671 - 9780992862688 - 9781538607510 ; , s. 2176-2180
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http://www.eurasip.o...
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https://hh.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.2...
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Abstract
Subject headings
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- In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local information and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms. © EURASIP 2017.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Keyword
- Biometrics
- Infrared devices
- Learning systems
- Neural networks
- Optical resolving power
- Quality of service
- Signal processing
- Convolutional neural network
- High resolution image
- Image super resolutions
- Iris recognition
- Learning approach
- Learning methods
- Local information
- Quality assessment
- Deep learning
Publication and Content Type
- ref (subject category)
- kon (subject category)
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