Fast Automatic Exposure Adjustment Method for Iris Recognition System
| Autor: | Lee Heejun, Alexey Mikhailovich Fartukov, Alexey Bronislavovich Danilevich, Odinokikh Gleb Andreevich, Kwang-Hyun Lee, Sergey Zavalishin, Eremeev Vladimir Alekseevich, Dae-Kyu Shin, Yoo Juwoan, Solomatin Ivan Andreevich, Xenya Petrova, Gnatyuk Vitaly Sergeevich |
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| Rok vydání: | 2019 |
| Předmět: |
Basis (linear algebra)
business.industry Computer science media_common.quotation_subject Iris recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Image (mathematics) Set (abstract data type) Face (geometry) 0202 electrical engineering electronic engineering information engineering Contrast (vision) 020201 artificial intelligence & image processing IRIS (biosensor) Computer vision Artificial intelligence Image sensor business media_common |
| Zdroj: | 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). |
| Popis: | In this paper, we propose a novel algorithm for automatic camera parameter adjustment, which is exploited for getting the correct image exposure required for iris recognition. We use two-step processing, where the first step adjusts the camera parameters on the basis of a single shot, and the second step applies precise iterative adjustment. In order to get the correct iris exposure, we use a weighted mask, which is constructed offline using a set of face images. In contrast to the existing algorithms, our method does not need to be calibrated for a particular camera sensor. We show that the proposed method significantly decreases false rejection rate caused by incorrect image exposure and reduces recognition time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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