FEATURE VECTOR CONSTRUCTION METHOD FOR IRIS RECOGNITION
| Autor: | Odinokikh Gleb Andreevich, Alexey Mikhailovich Fartukov, Mikhail Vladimirovich Korobkin, Yoo Juwoan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
021110 strategic
defence & security studies Engineering Biometrics business.industry urogenital system Pipeline (computing) Feature vector Iris recognition Feature extraction fungi 0211 other engineering and technologies Pattern recognition 02 engineering and technology urologic and male genital diseases female genital diseases and pregnancy complications Encoding (memory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision IRIS (biosensor) Artificial intelligence cardiovascular diseases Quantization (image processing) business |
| ISSN: | 2194-9034 |
| Popis: | One of the basic stages of iris recognition pipeline is iris feature vector construction procedure. The procedure represents the extraction of iris texture information relevant to its subsequent comparison. Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant. There are two characteristics which determine the vector element utility: fragility and discriminability. Conventional iris feature extraction methods consider the concept of fragility as the feature vector instability without respect to the nature of such instability appearance. This work separates sources of the instability into natural and encodinginduced which helps deeply investigate each source of instability independently. According to the separation concept, a novel approach of iris feature vector construction is proposed. The approach consists of two steps: iris feature extraction using Gabor filtering with optimal parameters and quantization with separated preliminary optimized fragility thresholds. The proposed method has been tested on two different datasets of iris images captured under changing environmental conditions. The testing results show that the proposed method surpasses all the methods considered as a prior art by recognition accuracy on both datasets. |
| Databáze: | OpenAIRE |
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