Finger knuckle print (FKP) as a physiological trait with a small image dimension, also a highly distinctive pattern, can be used as a reliable biometric identifier. In this paper, a new effective biometric authentication system using FKP texture based on relaxed local ternary pattern (RLTP) is presented. To further improve performance, cascading, overlapped patching and uniform rotation invariant pattern selection are used. Also to obtain more discriminative dominant patterns, an efficient learning framework is integrated with RLTP feature vectors. Identification and verification experiments conducted on the standard PolyU FKP dataset show the effectiveness of the proposed scheme.

指关节指纹（FKP）作为具有较小图像尺寸的生理特征，也是一种非常独特的图案，可以用作可靠的生物识别符。本文提出了一种基于松弛局部三元模式（RLTP）的基于FKP纹理的新型有效生物特征认证系统。为了进一步提高性能，使用了级联，重叠补丁和均匀旋转不变模式选择。同样，为了获得更具区分性的优势模式，将有效的学习框架与RLTP特征向量集成在一起。在标准PolyU FKP数据集上进行的识别和验证实验证明了该方案的有效性。