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.

本文旨在探讨使用深度神经网络提取固定长度嵌入的纹理信息，以减少指纹图像比对中的计算量，并研究不同传感器类型和指纹图像旋转平移对指纹嵌入提取的影响，实验结果表明，纹理嵌入512个特征元素为最佳选择，不同传感器类型表现存在差异。

跨不同嵌入尺寸和传感器类型基准测试固定长度指纹表示