Iris Feature Extraction and Matching Method for Mobile Biometric Applications
| Autor: | Solomatin Ivan Andreevich, Alexey Mikhailovich Fartukov, Efimov Iurii Sergeevich, Mikhail Vladimirovich Korobkin, Odinokikh Gleb Andreevich |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
021110 strategic
defence & security studies Matching (statistics) Biometrics business.industry Computer science Deep learning Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Field (computer science) Feature (computer vision) IRIS (biosensor) Artificial intelligence business Mobile device 0105 earth and related environmental sciences |
| Zdroj: | ICB |
| DOI: | 10.1109/icb45273.2019.8987379 |
| Popis: | Biometric methods are increasingly penetrating the field of mobile applications, confronting researchers with a huge number of problems that have not been considered before. Many different interaction scenarios in conjunction with the mobile device performance limitations challenge the capabilities of on-board biometrics. Saturated with complex textural features the iris image is used as a source for the extraction of unique features of the individual that are used for recognition. The mentioned features inherent to the interaction with the mobile device affect not only the source image quality but natural deformations of the iris leading to high intra-class variations and hence reducing the recognition performance. A novel method for iris feature extraction and matching is represented in this work. It is based on a lightweight CNN model combining the advantages of a classic approach and advanced deep learning techniques. The model utilizes shallow and deep feature representations in combination with characteristics describing the environment that helps to reduce intra-class variations and as a consequence the recognition errors. It showed high efficiency on the mobile and a few more datasets outperforming state-of-the-art methods by far. |
| Databáze: | OpenAIRE |
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