Likelihood Ratios for Deep Neural Networks in Face Comparison.

ABSTRACT: In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparency in the methods is essential. Consequently, state-of-the-art free software was preferred over commercial software. Three different open-source automated systems chosen for their availability and clarity were as follows: OpenFace, SeetaFace, and FaceNet; all three based on convolutional neural networks that return a distance (OpenFace, FaceNet) or similarity (SeetaFace). The returned distance or similarity is converted to a likelihood ratio using three different distribution fits: parametric fit Weibull distribution, nonparametric fit kernel de

SUBMITTER: Macarulla Rodriguez A 

PROVIDER: S-EPMC7383913 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature