Department of Automation, Tsinghua University, Beijing, China.
IEEE Trans Pattern Anal Mach Intell. 2012 Aug;34(8):1618-32. doi: 10.1109/TPAMI.2011.237.
During the past decade, many efforts have been made to use palmprints as a biometric modality. However, most of the existing palmprint recognition systems are based on encoding and matching creases, which are not as reliable as ridges. This affects the use of palmprints in large-scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to changes in age and skin conditions. Recently, several ridge-based palmprint matching algorithms have been proposed to fill the gap. Major contributions of these systems include reliable orientation field estimation in the presence of creases and the use of multiple features in matching, while the matching algorithms adopted in these systems simply follow the matching algorithms for fingerprints. However, palmprints differ from fingerprints in several aspects: 1) Palmprints are much larger and thus contain a large number of minutiae, 2) palms are more deformable than fingertips, and 3) the quality and discrimination power of different regions in palmprints vary significantly. As a result, these matchers are unable to appropriately handle the distortion and noise, despite heavy computational cost. Motivated by the matching strategies of human palmprint experts, we developed a novel palmprint recognition system. The main contributions are as follows: 1) Statistics of major features in palmprints are quantitatively studied, 2) a segment-based matching and fusion algorithm is proposed to deal with the skin distortion and the varying discrimination power of different palmprint regions, and 3) to reduce the computational complexity, an orientation field-based registration algorithm is designed for registering the palmprints into the same coordinate system before matching and a cascade filter is built to reject the nonmated gallery palmprints in early stage. The proposed matcher is tested by matching 840 query palmprints against a gallery set of 13,736 palmprints. Experimental results show that the proposed matcher outperforms the existing matchers a lot both in matching accuracy and speed.
在过去的十年中，人们已经付出了许多努力来使用掌纹作为生物识别模式。然而，大多数现有的掌纹识别系统都是基于编码和匹配折痕的，这些折痕不如脊线可靠。这影响了掌纹在大规模人员识别应用中的使用，在这些应用中，生物识别模式需要具有独特性，并且对年龄和皮肤状况的变化不敏感。最近，已经提出了几种基于脊线的掌纹匹配算法来填补这一空白。这些系统的主要贡献包括在存在折痕的情况下可靠地估计方向场，以及在匹配中使用多个特征，而这些系统中采用的匹配算法只是简单地遵循用于指纹的匹配算法。然而，掌纹在几个方面与指纹不同：1）掌纹更大，因此包含大量的细节点，2）手掌比指尖更具可变形性，3）掌纹不同区域的质量和区分能力差异很大。因此，尽管计算成本很高，这些匹配器仍然无法正确处理失真和噪声。受人类掌纹专家匹配策略的启发，我们开发了一种新的掌纹识别系统。主要贡献如下：1）定量研究了掌纹的主要特征的统计信息，2）提出了一种基于分段的匹配和融合算法来处理皮肤变形和不同掌纹区域的变化区分能力，3）为了降低计算复杂度，设计了一种基于方向场的注册算法，用于在匹配之前将掌纹注册到同一坐标系中，并构建级联滤波器在早期拒绝未匹配的图库掌纹。所提出的匹配器通过将 840 个查询掌纹与 13736 个掌纹图库进行匹配进行测试。实验结果表明，所提出的匹配器在匹配精度和速度方面都优于现有的匹配器。
