High-Performance Iris Recognition for Mobile Platforms
| Autor: | Odinokikh Gleb Andreevich, Mikhail Vladimirovich Korobkin, Michael N. Rychagov, Eremeev Vladimir Alekseevich, Gnatyuk Vitaly Sergeevich, Alexey Mikhailovich Fartukov |
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| Rok vydání: | 2018 |
| Předmět: |
Biometrics
Computer science business.industry Computation Feature vector Quantization (signal processing) Iris recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Key features Computer Graphics and Computer-Aided Design ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Iris image 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Mobile device |
| Zdroj: | Pattern Recognition and Image Analysis. 28:516-524 |
| ISSN: | 1555-6212 1054-6618 |
| Popis: | In spite of a fact that many standalone iris recognition solutions are successfully implemented and deployed around the world, development of a reliable iris recognition solution capable to provide high recognition performance (both in biometric quality and speed) on mobile device is still an actual task. Main issues related to iris recognition in the mobile devices consist in uncontrollable capturing conditions and limitations in computation power. The aim of the proposed approach is to eliminate aforementioned issues by providing user with comprehensive feedback and, at the same time, performing the most computationally complex operations only on the images of the best quality. Key features of the proposed approach are multi-stage algorithm structure, novel iris image quality estimation and adaptive iris feature vector quantization algorithms. These features allow to achieve high recognition accuracy and real-time performance which are proved by experimental results. |
| Databáze: | OpenAIRE |
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