Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Competitive Coding Scheme for Palmprint Verification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Ordinal Palmprint Represention for Personal Identification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Palmprint verification based on robust line orientation code
Pattern Recognition
A survey of palmprint recognition
Pattern Recognition
Texture-based palmprint retrieval using a layered search scheme for personal identification
IEEE Transactions on Multimedia
Characterization of palmprints by wavelet signatures via directional context modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Circuits and Systems for Video Technology
Consistency analysis on orientation features for fast and accurate palmprint identification
Information Sciences: an International Journal
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Palmprint identification system commonly stores multiple templates for each subject to improve the identification accuracy. The system then recognizes a query palmprint image by searching for its nearest neighbor from all of the templates. When applied on moderate or large scale identification system, it is often necessary to speed up this process. In this paper, to speed up the identification process, we propose to utilize the intrinsic characteristics of the templates of each subject to build a tree, and then perform fast nearest neighbor searching with assistance of the tree structure. Furthermore, we propose a novel method to generate the 'virtual' template from all the real templates of each subject. The tree constructed by the virtual template and the real templates can further speed up the identification process. Two representative coding-based methods, competitive code and ordinal code, are adopted to demonstrate the effectiveness of our proposed strategies. Using the Hong Kong PolyU palmprint database (version 2) and a large scale palmprint database, our experimental results show that the proposed method searches for nearest neighbors faster than brute force searching, and the speedup becomes larger when there are more templates per subject in the database. Results also show that our method is very promising for embedded system based moderate scale and PC based large scale identification systems.