標題: Heterogeneous iris image hallucination using sparse representation on a learned heterogeneous patch dictionary
作者: Li, Yung-Hui
Zheng, Bo-Ren
Ji, Dai-Yan
Tien, Chung-Hao
Liu, Po-Tsun
光電工程學系
Department of Photonics
關鍵字: sensor mis-match;patch-based heterogeneous dictionary;sparse representation
公開日期: 1-Jan-2014
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.
URI: http://dx.doi.org/10.1117/12.2060838
http://hdl.handle.net/11536/134711
ISBN: 978-1-62841-244-4
ISSN: 0277-786X
DOI: 10.1117/12.2060838
期刊: APPLICATIONS OF DIGITAL IMAGE PROCESSING XXXVII
Volume: 9217
起始頁: 0
結束頁: 0
Appears in Collections:Conferences Paper


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