Since hand-print recognition, i.e., palmprint, finger-knuckle-print (FKP), and hand-vein, have significant superiority in user convenience and hygiene, it has attracted greater enthusiasm from researchers. Seeking to handle the long-standing interference factors, i.e., noise, rotation, shadow, in hand-print images, multi-view hand-print representation has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. However, the existing methods usually ignore the high-order correlations between different views or fuse very limited types of features. To tackle these issues, in this paper, we present a novel tensorized multi-view low-rank approximation based robust hand-print recognition method (TMLA_RHR), which can dexterously manipulate the multi-view hand-print features to produce a high-compact feature representation. To achieve this goal, we formulate TMLA_RHR by two key components, i.e., aligned structure regression loss and tensorized low-rank approximation, in a joint learning model. Specifically, we treat the low-rank representation matrices of different views as a tensor, which is regularized with a low-rank constraint. It models the across information between different views and reduces the redundancy of the learned sub-space representations. Experimental results on eight real-world hand-print databases prove the superiority of the proposed method in comparison with other state-of-the-art related works.

中文翻译：

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基于张量多视图低秩逼近的鲁棒手印识别

由于手纹识别，即掌纹、指节纹（FKP）和手静脉在用户便利性和卫生方面具有显着优势，因此引起了研究人员的更大热情。为了解决手印图像中长期存在的干扰因素，即噪声、旋转、阴影，提出了多视图手印表示，通过利用不同视图的多个特征来增强特征表达。然而，现有的方法通常忽略不同视图之间的高阶相关性或融合非常有限的特征类型。为了解决这些问题，在本文中，我们提出了一种基于张量化多视图低秩近似的鲁棒手印识别方法（TMLA_RHR），该方法可以灵巧地操纵多视图手印特征以产生高紧凑的手印识别方法。特征表示。为了实现这一目标，我们在联合学习模型中通过两个关键组件来制定 TMLA_RHR，即对齐结构回归损失和张量化低秩近似。具体来说，我们将不同视图的低秩表示矩阵视为张量，并使用低秩约束对其进行正则化。它对不同视图之间的跨信息进行建模，并减少学习的子空间表示的冗余。八个真实世界手印数据库的实验结果证明了该方法与其他最先进的相关工作相比的优越性。