期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：70
期号：1
出版社：Journal of Theoretical and Applied
摘要：This paper proposes an iris classification for small and imbalance dataset employing traditional Support Vector Machine classifier. A technique from the combination of modified conventional moving average and histogram equalization are proposed to produce the figurative and interpretability iris texture. In doing so, the smooth effect of the iris texture will take place for feature extraction. This study also proposed one-dimensional features with dynamic vector which are extracted by manipulating the global mean and intensity variation on the un-normalized iris image. The uniqueness of the proposed feature extraction technique is where eyelid, eyelashes and lighting effect will be merged together in the calculation in order to produce the feature vector. Therefore, this study has not considered the noise removal method, and this differs compared to most previous works, which have implemented noise removal method for eliminating the eyelid and eyelashes information. The images from CASIA Version 1 and Version 4-Interval are used to assess the proposed method. The results obtained on these data sets reveal the effectiveness of the suggested method.
关键词：Iris Classification; Support Vector Machines; Global Mean; One-dimensional
