Traditional minutiae-based fingerprint representations consist of a variable-length set of minutiae. This necessitates a more complex comparison causing the drawback of high computational cost in one-to-many comparison. Recently, deep neural networks have been proposed to extract fixed-length embeddings from fingerprints. In this paper, we explore to what extent fingerprint texture information contained in such embeddings can be reduced in terms of dimension while preserving high biometric performance. This is of particular interest since it would allow to reduce the number of operations incurred at comparisons. We also study the impact in terms of recognition performance of the fingerprint textural information for two sensor types, i.e. optical and capacitive. Furthermore, the impact of rotation and translation of fingerprint images on the extraction of fingerprint embeddings is analysed. Experimental results conducted on a publicly available database reveal an optimal embedding size of 512 feature elements for the texture-based embedding part of fixed-length fingerprint representations. In addition, differences in performance between sensor types can be perceived.

翻译：传统的基于细节点的指纹表示由一组可变长度的细节集组成，这导致了更复杂的比较，在1:N比较中造成高计算成本的缺点。近年来，深度神经网络被提出用于从指纹中提取固定长度的嵌入。本文探索了此类嵌入中包含的指纹纹理信息在维度上可缩减的程度，同时保持较高的生物特征性能。这具有重要意义，因为它能够减少比较中产生的计算操作次数。我们还研究了两种传感器类型（即光学传感器和电容传感器）的指纹纹理信息对识别性能的影响。此外，分析了指纹图像的旋转和平移对指纹嵌入提取的影响。在公开数据库上进行的实验结果表明，对于固定长度指纹表示的纹理嵌入部分，最优嵌入大小为512个特征元素。同时，不同传感器类型之间的性能差异也可以被观察到。