Heterogeneous iris image hallucination using sparse representation on a learned heterogeneous patch dictionary

Yung Hui Li, Bo Ren Zheng, Dai Yan Ji, Chung-Hao Tien, Po-Tsun Liu

研究成果: Conference contribution同行評審

摘要

Cross sensor iris matching may seriously degrade the recognition performance because of the sensor mis-match problem of iris images between the enrollment and test stage. In this paper, we propose two novel patch-based heterogeneous dictionary learning method to attack this problem. The first method applies the latest sparse representation theory while the second method tries to learn the correspondence relationship through PCA in heterogeneous patch space. Both methods learn the basic atoms in iris textures across different image sensors and build connections between them. After such connections are built, at test stage, it is possible to hallucinate (synthesize) iris images across different sensors. By matching training images with hallucinated images, the recognition rate can be successfully enhanced. The experimental results showed the satisfied results both visually and in terms of recognition rate. Experimenting with an iris database consisting of 3015 images, we show that the EER is decreased 39.4% relatively by the proposed method.

原文English
主出版物標題Applications of Digital Image Processing XXXVII
編輯Andrew G. Tescher
發行者SPIE
ISBN(電子)9781628412444
DOIs
出版狀態Published - 1 1月 2014
事件Applications of Digital Image Processing XXXVII - San Diego, 美國
持續時間: 18 8月 201421 8月 2014

出版系列

名字Proceedings of SPIE - The International Society for Optical Engineering
9217
ISSN(列印)0277-786X
ISSN(電子)1996-756X

Conference

ConferenceApplications of Digital Image Processing XXXVII
國家/地區美國
城市San Diego
期間18/08/1421/08/14

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