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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Multifeature-Based High-Resolution Palmprint Recognition
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Multifeature-Based High-Resolution Palmprint Recognition

机译：基于多功能的高分辨率掌纹识别

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摘要

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.
机译：Palmprint是一种有前途的生物识别功能，可用于访问控制和取证应用。先前关于掌纹识别的研究主要集中在低分辨率（约100 ppi）的掌纹上。但是对于高安全性应用（例如，法医使用），需要高分辨率掌纹（500 ppi或更高），可以从中提取更多有用的信息。在本文中，我们提出了一种新的高分辨率掌纹识别算法。所提出的算法的主要贡献包括以下内容：1）使用多个特征，即细节，密度，方向和主线，来进行掌纹识别，以显着提高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，该算法在折痕较大的区域中的性能要优于现有算法。 3）将新颖的融合方案用于身份识别应用程序，该方案的性能要优于常规融合方法，例如加权和规则，SVM或Neyman-Pearson规则。此外，我们分析了不同特征组合的判别力，发现密度对于掌纹识别非常有用。在包含14,576张完整掌纹的数据库上的实验结果表明，该算法取得了良好的性能。在验证的情况下，在识别实验中，识别系统的错误拒绝率（FRR）为16％，比错误接受率（FAR）为10 ^ {-5}时的最佳现有算法低17％。 ，第一级实时扫描部分掌纹识别率从82.0提高到91.7％。

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