Multifeature-Based High-Resolution Palmprint Recognition
Multifeature-Based High-Resolution Palmprint Recognition
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基于多特征的高分辨率掌纹识别
DOI：
10.1109/tpami.2010.164
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发表时间：
2011-05-01
影响因子：
23.6
通讯作者：
Zhou, Jie
中科院分区：
文献类型：
--
作者：
Dai, Jifeng;Zhou, Jie
掌纹是一种很有前途的生物特征，可用于门禁控制和取证应用。以往对掌纹识别的研究主要集中在低分辨率(约100ppi)的掌纹上。但对于高度安全的应用(例如，法医应用)，需要高分辨率(500ppi或更高)的掌纹，从中可以提取更有用的信息。本文提出了一种新的高分辨率掌纹识别算法。该算法的主要贡献包括：1)将细节点、密度、方向和主线等多个特征用于掌纹识别，显著提高了传统算法的匹配性能。2)设计了一种基于质量的自适应方向场估计算法，该算法在具有大量折痕的区域中比现有算法具有更好的性能。3)将一种新的融合方案用于识别应用，其性能优于传统的融合方法，例如加权和规则、支持向量机或Neyman-Pearson规则。此外，我们还分析了不同特征组合的区分能力，发现密度对掌纹识别非常有用。在包含14,576个完整掌纹的数据库上的实验结果表明，该算法取得了较好的性能。在验证的情况下，识别系统的误拒率(FRR)为16%，在误接受率(FAR)为10(-5)的情况下比现有的最佳算法低17%，而在识别实验中，RANK-1实时扫描部分掌纹识别率从82.0%提高到91.7%。
Palmprint is a promising biometric feature for use in access control and forensic applications. Previous research on palmprint recognition mainly concentrates on low-resolution (about 100 ppi) palmprints. But for high-security applications (e. g., forensic usage), high-resolution palmprints (500 ppi or higher) are required from which more useful information can be extracted. In this paper, we propose a novel recognition algorithm for high-resolution palmprint. The main contributions of the proposed algorithm include the following: 1) use of multiple features, namely, minutiae, density, orientation, and principal lines, for palmprint recognition to significantly improve the matching performance of the conventional algorithm. 2) Design of a quality-based and adaptive orientation field estimation algorithm which performs better than the existing algorithm in case of regions with a large number of creases. 3) Use of a novel fusion scheme for an identification application which performs better than conventional fusion methods, e. g., weighted sum rule, SVMs, or Neyman-Pearson rule. Besides, we analyze the discriminative power of different feature combinations and find that density is very useful for palmprint recognition. Experimental results on the database containing 14,576 full palmprints show that the proposed algorithm has achieved a good performance. In the case of verification, the recognition system's False Rejection Rate (FRR) is 16 percent, which is 17 percent lower than the best existing algorithm at a False Acceptance Rate (FAR) of 10(-5), while in the identification experiment, the rank-1 live-scan partial palmprint recognition rate is improved from 82.0 to 91.7 percent.