Palmprint Recognition Based on Regional Rank Correlation of Directional Features

Authors:
Yufei Han;Zhenan Sun;Tieniu Tan;Ying Hao
Affiliations:
Center for Biometrics and Security Research, National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences,;Center for Biometrics and Security Research, National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences,;Center for Biometrics and Security Research, National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences,;Center for Biometrics and Security Research, National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences,
Venue:
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Year:
2009

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Abstract

Automatic personal identification based on palmprints has been considered as a promising technology in biometrics family during recent years. In pursuit of accurate palmprint recognition approaches, it is a key issue to design proper image representation to describe skin textures in palm regions. According to previous achievements, directional texture measurement provides a powerful tool for depicting palmprint appearances. Most of successful approaches can be ranged into this framework. Following this idea, we propose a novel palmprint representation in this paper, which describes palmprint images by constructing rank correlation statistics of appearance patterns within local image areas. Promising experimental results on two large scale palmprint databases demonstrate that the proposed method achieves even better performances than the state-of-the-art approaches.