Abstract Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past decade, and recently three dimensional (3D) palmprint recognition Accurate and robust personal authentication is a crucial issue in the modern e-world, and biometric techniques are promising solutions to this problem. Palmprint is a relatively new but important member of biometric characteristics. It contains a rich amount of stable texture features, which lead to very high recognition accuracy. Meanwhile, the merits such as low-cost, user friendliness and high matching speed make it practicable to use in a large scale. Many methods have been proposed for 2D palmprint recognition [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] . In general, the current 2D palmprint recognition methods can be classified into three categories: coding-based methods [3, 4, 6, 8, 13] Such methods do not use the prior knowledge of the object but they need a training process. Although 2D palmprint recognition has achieved high accuracy, it still has some drawbacks. For example, 2D palmprint is easy to be counterfeited, and the image quality can be much affected by illumination changes and scrabbling. To overcome these problems of 2D palmprint recognition, 3D palmprint recognition techniques have been recently developed [18] , where the 3D palmprint data are captured by using structured-light imaging [20] . In the 3D palmprint system [18] , the depth information of the palm inner surface is collected, and a curvature-based method is used for 3D palmprint feature extraction and matching. Since the 2D and 3D palmprint images can be captured simultaneously, the 2D and 3D palmprint features can be readily fused. In [18] and [19], some straightforward fusing methods were used and better results than using 2D or 3D features only were obtained. This paper aims to develop an efficient 2D and 3D palmprint matching scheme for more accurate and robust personal authentication. To better use the 2D and 3D palmprint information, we propose a couple of novel feature matching rules. We classify the palmprint features into three different levels: shape level, line level and texture
In REF , Li et al. extracted three levels of 2D and 3D palmprint features, including shape features, principal line features, and texture features.
Efficient joint 2D and 3D palmprint matching with alignment refinement
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Computer Science