Benchmarking fixed-length Fingerprint Representations across different
  Embedding Sizes and Sensor Types
Tim Rohwedder Daile Osorio-Roig Christian Rathgeb Christoph Busch
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.