Contactless 3D finger knuckle is a new biometric identifier which can offer an accurate, efficient and convenient alternative for the personal identification. The current 3D finger knuckle recognition methods are limited by computationally complex or inefficient matching algorithms, which attempt to compute the matching scores from all possible translational and rotational parameters for matching a pair of templates. The strength of such approach lies in its simplicity and reliability for accurately matching intra-class samples, but expensive computational time is required. Furthermore, attempting on excessive numbers of translational and rotational parameters can also degrade the overall recognition accuracy because the imposter matches can be increased. In fact, this conventional matching approach is commonly adopted in many biometric studies, but its drawbacks have not received adequate attention. This article addresses such 3D finger knuckle recognition problem by developing a more efficient matching approach using surface key points extracted from 3D finger knuckle surfaces. Our comparative experimental results with the state-of-the art method on a publicly available 3D finger knuckle database indicates that our approach can offer over 23 times faster with performance improvement on the accuracy. Although the focus of our work is on 3D finger knuckle recognition, we also present the performance of our method on other publicly available databases with similar 3D biometric patterns including 3D palmprint and 3D fingerprint, to validate the effectiveness of the proposed approach.

中文翻译：

---

利用表面关键点进行高效、准确的 3D 指关节匹配


非接触式3D指关节是一种新型生物识别技术，可以为个人身份识别提供准确、高效、便捷的替代方案。当前的 3D 指关节识别方法受到计算复杂或低效的匹配算法的限制，这些算法试图根据所有可能的平移和旋转参数来计算匹配分数以匹配一对模板。这种方法的优点在于其简单性和可靠性，可以准确匹配类内样本，但需要昂贵的计算时间。此外，尝试过多的平移和旋转参数也会降低整体识别精度，因为冒名顶替者匹配会增加。事实上，这种传统的匹配方法在许多生物识别研究中普遍采用，但其缺点并未得到足够的重视。本文通过使用从 3D 指节表面提取的表面关键点开发更有效的匹配方法来解决此类 3D 指节识别问题。我们在公开的 3D 指关节数据库上与最先进的方法进行的比较实验结果表明，我们的方法可以将速度提高 23 倍以上，并且精度方面的性能提高。虽然我们工作的重点是 3D 指关节识别，但我们还在具有类似 3D 生物识别模式（包括 3D 掌纹和 3D 指纹）的其他公开数据库上展示了我们的方法的性能，以验证所提出方法的有效性。