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Learning binary hash codes for finger vein image retrieval

机译：学习二进制哈希码以获取手指静脉图像

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摘要

High dimensional real-valued features have been shown to be effective for finger vein identification, but result in high computational cost especially in a large-scale finger vein database. Therefore compact binary codes are generally used to obtain fast query speed as well as reduce storage requirements. However, only a few strategies are available for finger vein retrieval in Hamming space, and existing binary representation of finger veins can become unstable and undiscriminating due to finger vein variations induced by translation, rotation and illumination. In this paper, we propose a binary hash codes learning algorithm to map finger vein images in the original feature space to Hamming space. First, to obtain the discriminative finger vein image features, a novel finger vein image representation method called Nonlinearly Subspace Coding (NSC) is proposed. The codebook is a union of low-dimensional linear subspaces instead of visual words. For a given local finger vein texton, its top-k nearest subspaces are found and the texton is nonlinearly mapped into these subspaces. Then, a finger vein Binary Hash Codes (BHC) learning method is proposed by jointly considering the discriminability and the stability of the binary code. Experimental results on a two-session public finger vein database and a large fused finger vein database demonstrate the effectiveness and efficiency of our binary hash coding learning algorithm for large-scale finger vein retrieval. (C) 2018 Elsevier B.V. All rights reserved.
机译：高维实值特征已被证明可有效用于手指静脉识别，但会导致较高的计算成本，尤其是在大型手指静脉数据库中。因此，紧凑的二进制代码通常用于获得快速查询速度以及减少存储需求。然而，在汉明空间中只有很少的策略可用于手指静脉的检索，并且由于平移，旋转和照明引起的手指静脉变化，现有的手指静脉二进制表示形式可能变得不稳定且无法区分。在本文中，我们提出了一种二进制哈希码学习算法，将原始特征空间中的手指静脉图像映射到汉明空间。首先，为了获得具有区别性的手指静脉图像特征，提出了一种新颖的手指静脉图像表示方法，称为非线性子空间编码（NSC）。该码本是低维线性子空间的并集，而不是视觉单词。对于给定的局部指静脉texton，找到其前k个最近的子空间，并将该texton非线性映射到这些子空间中。然后，结合二进制码的可识别性和稳定性，提出了一种手指静脉二进制哈希码（BHC）的学习方法。在两个阶段的公共手指静脉数据库和大型融合手指静脉数据库上的实验结果证明了我们的二进制哈希编码学习算法用于大规模手指静脉检索的有效性和效率。 （C）2018 Elsevier B.V.保留所有权利。

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