标题:A Fast Eyelash Detection Algorithm Based on Morphological Operation
作者:Gao, Shuqin ;Han, Min ;Wang, Deqiang ;Wang, Meng
作者机构:[Gao, Shuqin ;Han, Min ;Wang, Deqiang ;Wang, Meng ] School of Information Science and Engineering, Shandong University, Qingdao, China
会议名称:12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
会议日期:19 October 2019 through 21 October 2019
来源:Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
出版年:2019
DOI:10.1109/CISP-BMEI48845.2019.8965795
关键词:biometrics recognition; eyelash detection; iris image; iris recognition; morphological operation
摘要:Iris recognition is regarded as a promising biometric recognition technology. However, iris recognition schemes suffer from the interference from eyelashes. Intuitively, it is a crucial issue to detect the eyelash precisely in the preprocessing stage of iris recognition. In this paper, a novel eyelash detection algorithm in iris image based on morphological operation is proposed. Given an iris image, the effective search area of the eyelash is determined based on the position of the pupil; Then, morphological closing operation is performed on the effective search area of the eyelash and thereby a difference image is got. After that, the difference image is segmented by using an optimal threshold which is obtained by using maximum interclass variance method and thus candidate eyelash pixels are determined. Finally, based on the candidate eyelash pixels, a two-stage eyelash detection process is carried out to figure out the real eyelash pixels. Experimental results suggest that the proposed algorithm can achieve good effect of eyelash detection, and compared with the previous methods, the detection speed is greatly increased, it meets the requirements of speed and accuracy during the image preprocessing stage. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079144457&doi=10.1109%2fCISP-BMEI48845.2019.8965795&partnerID=40&md5=4d0790cf88606d9813078a3a58ac3570
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