文章基本信息

标题：Principal Component Analysis - Based EthnicityPrediction Using Iris Feature
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作者：B. M. Latinwo ; A. S. Falohun ; E. O. Omidiora 等
期刊名称：Current Journal of Applied Science and Technology
印刷版ISSN：2457-1024
出版年度：2016
卷号：16
期号：4
页码：1-5
语种：English
出版社：Sciencedomain International
摘要：This paper presents the effectiveness of Principal Component Analysis (PCA) technique in analyzing iris texture by performing dimensionality reduction and extracting unique feature codes of images for efficient ethnicity classification using iris images from African and two Asian datasets. Three hundred and thirty-six iris images were obtained, preprocessed (enhanced) and segmented for easy identification of unique features using Histogram Equalization and Hough Transform techniques, respectively. Feature dimensionality reduction and extraction of feature codes of the segmented images was carried out using PCA while the result showed the similarities and differences between irises of different ethnicities based on these generated codes. The research established a very close similarity of the Asia1 and Asia2 irises, due to the classification of their features in the same feature code subrange. Also, few images from Asia1 and Asia2 were classified with the Africans which explained the possibility of mixed race of subjects through inter-marriage.
关键词：Ethnicity;Iris;feature codes;PCA;classification