Abstract
Iris recognition is one of the most accurate biometric technologies. Iris images are easily blurred because of motions or defocusing. Blurring iris image easily leads to mismatch in recognition. So, these blurred iris images should be excluded before recognition to improve the recognition performance. This paper presents a blurring detection method for iris image based on local feature. The proposed method firstly employs RST (Radial Symmetry Transform) to localize pupil boundary. Then 32 indicators are calculated on several ROIs (Region of Interest) for each iris. After that, LASSO (Least Absolute Shrinkage and Selectionator Operator) is used to select features. Finally, SVM (Support Vector Machine) algorithm is used to separate the blurred iris images from ideal ones based on the selected 14 indicators. The developed method is experimented on a self-built database. And the correct classification rate reaches 97.87%.
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Acknowledgement
This research is partly supported by National Natural Science Funds of China, No. 61703088, the Doctoral Scientific Research Foundation of Liaoning Province, No. 20170520326 and “the Fundamental Research Funds for the Central Universities”, N160503003.
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Wang, J., Wang, Q., Zhou, Y., Chu, Y., Zhang, X. (2020). Blurring Detection Based on Selective Features for Iris Recognition. In: Qian, J., Liu, H., Cao, J., Zhou, D. (eds) Robotics and Rehabilitation Intelligence. ICRRI 2020. Communications in Computer and Information Science, vol 1335. Springer, Singapore. https://doi.org/10.1007/978-981-33-4929-2_15
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DOI: https://doi.org/10.1007/978-981-33-4929-2_15
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