A novel hierarchical approach for multispectral palmprint recognition
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Palmprint is one important biometric feature with uniqueness, stability and high distinguishability, and its study has attracted much attention in the past decades. Although many palmprint-based recognition methods have been proposed and successfully applied for identity authentication, most of the previous researches usually only use the images captured in natural light. It is hard, if not impossible, for further improvement of recognition accuracy based on these palmprint images due to limitations of using the natural light. In order to obtain high recognition rate with more discriminative information, we propose to use multispectral palmprint instead of natural light palmprint in this paper, and develop a multispectral palmprint recognition method based on a hierarchical idea. First, we extract the Block Dominant Orientation Code (BDOC) as a rough feature, and the Block-based Histogram of Oriented Gradient (BHOG) as a fine feature. Second, a hierarchical recognition approach is proposed based on these two types of features. Technically, we fuse different features obtained from different bands in the proposed scheme in order to improve the recognition accuracy. Finally, experimental results show that the recognition accuracy of the proposed method is not only superior to previous high-performance methods based on the PolyU palmprint database with the natural light but also it can further improve the state of the art performance achieved by some approaches based on the PolyU multispectral palmprint database.
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