Palmprint is one of the most unique and stable biometric characteristics. Although 2D palmprint recognition can achieve high accuracy, the 2D palmprint images can be easily counterfeited and much 3D depth information is lost in the imaging process. This paper presents a new approach, 3D palmprint recognition, to exploit the 3D structural information of the palm surface. The structured-light imaging is used to acquire the 3D palmprint data, from which the features of Mean Curvature, Gauss

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... re and Surface Type (ST) are extracted. A fast feature matching and score level fusion strategy are then used to classify the input 3D palmprint data. With the established 3D palmprint database, a series of verification and identification experiments are conducted and the results show that 3D palmprint technique can achieve high recognition rate while having high anti-counterfeiting capability.