BIOMETRIC PERSONAL IDENTIFICATION ON 2D WAVELET TRANSFORM AND CHI-SQUARED MODEL
| Autor: | Iwasokun B. Gabriel, Fasoranbaku A. Olusoga, AdeniyiTunde Taiwo, Olatubosun Olabode, Oluwadare A. Samuel |
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| Rok vydání: | 2015 |
| Předmět: |
Engineering
Biometrics urogenital system business.industry fungi Feature extraction Iris recognition Wavelet transform Pattern recognition urologic and male genital diseases female genital diseases and pregnancy complications Hough transform law.invention medicine.anatomical_structure law medicine Preprocessor Computer vision Segmentation cardiovascular diseases Artificial intelligence Iris (anatomy) business |
| Zdroj: | INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY. 14:6074-6084 |
| ISSN: | 2277-3061 |
| DOI: | 10.24297/ijct.v14i9.3984 |
| Popis: | Iris recognition system consists of image acquisition, iris preprocessing, iris segmentation and feature extraction with comparism (matching) stages. The biometric based personal identification using iris requires accurate iris segmentation for successful identification or recognition. Recently, several researchers have implemented various methods for segmentation of boundaries which will require a modification of some of the existing segmentation algorithms for their proper recognition. Therefore, this research presents a 2D Wavelet Transform and Chi-squared model for iris features extraction and recognition. Circular Hough Transform was used for the segmentation of the iris image. The system localizes the circular iris and pupil region and removes the occluding eyelids and eyelashes. The extracted iris region is normalized using Daugman’s rubber sheet model into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data (iris signature) from the 2D wavelet transform data is extracted, forming the biometric template. The chi-squared distance is employed for classification of iris templates and recognition. Implementing this model can enhance identification. Based on the designed system, an FAR (False Acceptances ratio) of 0.00 and an FRR (False Rejection Ratio) of 0.896 was achieved. |
| Databáze: | OpenAIRE |
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