Ordinal palmprint represention for personal identification [represention read representation]

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 279 - 284 vol. 1
• Main Authors: Zhenan Sun, Tieniu Tan, Yunhong Wang, Li, S.Z.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Automation; Biometrics; Computer science; Computer vision; Face recognition; Feature extraction; Fingerprint recognition; Laboratories; National security; Pattern recognition
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2005.267

Palmprint-based personal identification, as a new member in the biometrics family, has become an active research topic in recent years. Although great progress has been made, how to represent palmprint for effective classification is still an open problem. In this paper, we present a novel palmprint representation - ordinal measure, which unifies several major existing palmprint algorithms into a general framework. In this framework, a novel palmprint representation method, namely orthogonal line ordinal features, is proposed. The basic idea of this method is to qualitatively compare two elongated, line-like image regions, which are orthogonal in orientation and generate one bit feature code. A palmprint pattern is represented by thousands of ordinal feature codes. In contrast to the state-of-the-art algorithm reported in the literature, our method achieves higher accuracy, with the equal error rate reduced by 42% for a difficult set, while the complexity of feature extraction is halved.