NDLI: Sift-based image alignment for contactless palmprint verification

Content Provider	IEEE Xplore Digital Library
Author	Qiushi Zhao Wei Bu Xiangqian Wu
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol. Harbin, Harbin, China (Qiushi Zhao; Xiangqian Wu) \|\| Dept. of New Media, Harbin Inst. of Technol. Harbin, Harbin, China (Wei Bu)
Abstract	Contactless palmprint recognition is an effective way to improve the user-friendliness of palmprint recognition technology. The main challenge of contactless palmprint recognition is the intra-class variations caused by contact-less image acquisition. In such occasions, traditional palm-print recognition algorithms which require precise image alignment may not contribute. Aiming at solving this problem, this paper proposes a contactless palmprint recognition method with a precise palmprint image alignment. The original contactless palmprint images are firstly aligned using a projective transformation model that estimated from matched SIFT feature points. From the aligned images, a prominent palmprint feature representation method, the competitive code, is extracted and matched. Finally, matching scores of both SIFT and competitive code are fused to further improve the accuracy. Experiments on a public contactless palmprint database show that after the image alignment, the verification accuracy of competitive code has increased dramatically, and the result is further enhanced by fusing the matching scores of competitive code and SIFT features.
Starting Page	1
Ending Page	6
File Size	332036
Page Count	6
File Format	PDF
ISBN	9781479903108
DOI	10.1109/ICB.2013.6612989
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-04
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Image recognition Databases Imaging Feature extraction Educational institutions Gabor filters
Content Type	Text
Resource Type	Article

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