Deep Ternary Hashing Code for Palmprint Retrieval and Recognition

Qizhou Lin, L. Leng, M. Khan
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Abstract

Hashing has received more and more attention due to the characteristics of small storage and fast retrieval, especially in the field of biometric computing. However, there are only two types of binary relations, logical 'true' and logical 'false', which can't be distinguished well at the demarcation of these two logical relations in the Hamming space, resulting in ambiguity in the Hamming space neighborhood. Therefore, deep hash network is used to extract palmprint feature and optimize the ambiguity in Hamming space by using mutual information (MI) to obtain a trivialized palmprint hash code. The tri-valued Hamming distance using Kleene logic for matching reduces storage and improves matching speed compared to traditional local feature-based coding methods, and outperforms the binary palmprint deep hash coding. All the experiments are conducted on several contact/contactless palmprint and palm vein libraries, and extensive comparisons are made with several state-of-the-art methods, and the results demonstrate the effectiveness of the proposed scheme.
掌纹检索与识别的深度三元哈希代码
哈希算法以其存储空间小、检索速度快的特点受到越来越多的关注,特别是在生物特征计算领域。然而,二元关系只有逻辑“真”和逻辑“假”两种类型,这两种逻辑关系在汉明空间的分界处不能很好地区分,导致汉明空间邻域产生歧义。因此,利用深度哈希网络提取掌纹特征,并利用互信息(MI)优化汉明空间中的歧义,得到一个简化的掌纹哈希码。与传统的基于局部特征的编码方法相比,使用Kleene逻辑进行匹配的三值汉明距离减少了存储空间,提高了匹配速度,并且优于二进制掌纹深度哈希编码。所有的实验都在几个接触式/非接触式掌纹和掌纹库上进行,并与几种最先进的方法进行了广泛的比较,结果表明了所提出方案的有效性。
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