Palmprint recognition has been widely applied in security and, particularly, authentication. In the past decade, various palmprint recognition methods have been proposed and achieved promising recognition performance. However, most of these methods require rich a priori knowledge and cannot adapt well to different palmprint recognition scenarios, including contact-based, contactless, and multispectral palmprint recognition. This problem limits the application and popularization of palmprint recognition. In this article, motivated by the least square regression, we propose a salient and discriminative descriptor learning method (SDDLM) for general scenario palmprint recognition. Different from the conventional palmprint feature extraction methods, the SDDLM jointly learns noise and salient information from the pixels of palmprint images, simultaneously. The learned noise enforces the projection matrix to learn salient and discriminative features from each palmprint sample. Thus, the SDDLM can be adaptive to multiscenarios. Experiments were conducted on the IITD, CASIA, GPDS, PolyU near infrared (NIR), noisy IITD, and noisy GPDS palmprint databases, and palm vein and dorsal hand vein databases. It can be seen from the experimental results that the proposed SDDLM consistently outperformed the classical palmprint recognition methods and state-of-the-art methods for palmprint recognition.

中文翻译：

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学习用于掌纹特征提取和识别的显着和判别描述符。

掌纹识别已广泛应用于安全性，尤其是身份验证。在过去的十年中，已经提出了各种掌纹识别方法并且获得了有希望的识别性能。但是，大多数这些方法需要丰富的先验知识，并且不能很好地适应不同的掌纹识别方案，包括基于接触的，非接触式和多光谱掌纹识别。该问题限制了掌纹识别的应用和普及。在本文中，受最小二乘回归的启发，我们提出了一种针对一般场景掌纹识别的显着和判别性描述符学习方法（SDDLM）。与传统的掌纹特征提取方法不同，SDDLM可以从掌纹图像的像素中共同学习噪声和显着信息，同时。获悉的噪声将强制投影矩阵以从每个掌纹样本中学习显着和有区别的特征。因此，SDDLM可以适应多种情况。实验在IITD，CASIA，GPDS，PolyU近红外（NIR），嘈杂的IITD和嘈杂的GPDS掌纹数据库以及掌静脉和手背静脉数据库上进行。从实验结果可以看出，所提出的SDDLM始终优于经典的掌纹识别方法和最新的掌纹识别方法。和嘈杂的GPDS掌纹数据库，以及掌静脉和手背静脉数据库。从实验结果可以看出，所提出的SDDLM始终优于经典的掌纹识别方法和最新的掌纹识别方法。和嘈杂的GPDS掌纹数据库，以及掌静脉和手背静脉数据库。从实验结果可以看出，所提出的SDDLM始终优于经典的掌纹识别方法和最新的掌纹识别方法。