Palmprint Recognition Based on Regional Rank Correlation of ...
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Palmprint Recognition Based on Regional Rank Correlation of ...
Palmprint Recognition Based on Regional Rank. Correlation of Directional Features. Yufei Han, Zhenan Sun, Tieniu Tan, and Ying Hao. Center for Biometrics ...
Palmprint Recognition Based on Regional Rank Correlation of Directional Features Yufei Han, Zhenan Sun, Tieniu Tan, and Ying Hao Center for Biometrics and Security Research, National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences {yfhan,znsun,tnt,yhao}@nlpr.ia.ac.cn
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.
1 Introduction Palmprint recognition technology distinguishes one individual from others based on differences of skin appearances in central palm regions of hands [1]. According to previous work, most of discriminating line-like image patterns in palmprints can be captured using only a low-resolution imaging device, like web cameras or low-cost CCD cameras. Due to its applicability, palmprint recognition has attracted increasing attention during recent years, especially in civil use, such as airport and custom. In this paper, we focus on palmprint analysis based on low-resolution (
