Contactless 3D finger knuckle pattern is a new biometric identifier which offers highly discriminative features for the finger knuckle based personal identification. State-of-the-art methods for object recognition, a more generic problem, employ deep neural network based approaches and demonstrate superior effectiveness. However, any direct applications from those methods do not outperform specialized hand-crafted feature description approaches for the problem addressed in this paper. In addition, such deep neural network based methods have to address challenges associated with emerging biometrics, e.g. availability of very limited training data, large intra-class or train-test sample variations as observed for the real applications, etc. This paper attempts to address the above challenges and introduces a new deep neural network based approach for the contactless 3D finger knuckle identification. Our approach simultaneously encodes and incorporates deep features from multiple scales to form a more robust deep feature representation. Such collaborative feature representations are robustly matched using an efficient alignment scheme with a fully convolutional architecture to accommodate involuntary finger variations during the contactless imaging. Comparative experimental results in the two-session 3D finger knuckle images database, acquired from over 200 subjects and is publicly introduced from this paper, illustrate superior performance over the state-of-the-art methods, e.g. offering ~22% GAR improvement at extremely low FAR under challenging comparison scenarios. Additional experiments in other publicly available databases including 3D palmprint, 3D fingerprint, and 2D finger knuckle further validate the effectiveness and demonstrate the generalizability of the proposed approach.

中文翻译：

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深度特征协作解决3D指关节识别难题

非接触式3D指关节图案是一种新的生物识别符，可为基于指关节的个人识别提供高度区分性的功能。最先进的对象识别方法（一个更常见的问题）采用了基于深度神经网络的方法，并显示出卓越的有效性。但是，对于这些问题，这些方法的任何直接应用都不会胜过专门的手工特征描述方法。此外，这种基于深度神经网络的方法必须解决与新兴生物识别技术相关的挑战，例如，非常有限的训练数据的可用性，针对实际应用所观察到的大量课内或训练测试样本变化等。本文试图解决上述挑战，并介绍了一种新的基于深度神经网络的非接触式3D指关节识别方法。我们的方法同时编码和合并了多个尺度的深度特征，以形成更强大的深度特征表示。此类协作特征表示使用有效的对齐方案与完全卷积的体系结构进行了稳健的匹配，以适应非接触式成像过程中不自主的手指变化。从200多名受试者那里获得的两阶段3D指关节图像数据库中的比较实验结果已从本文中公开介绍，这些实验结果显示了优于最新方法的性能，例如，在极端情况下可将GAR提高约22％在具有挑战性的比较方案下的FAR低。