Deep learning features in facial identification and the likelihood ratio bound

Abstract

In recent years, the score-based likelihood ratio (SLR) method for facial comparison has attracted considerable research attention. This method relies on the match scores that are calculated from the features obtained from facial recognition systems, deep learning based in particular. However, this concept has not been completely understood. Therefore, this study is aimed at investigating deep learning facial features, and the SLR levels of their match scores. We propose a new interpretation that the deep learning feature is a class characteristic. Based on a large-scale data set experiment, we present evidence that the log SLR value of deep learning features can reach 8 in some data sets. The study results imply that the SLR of deep learning features is a useful method for facial identification, especially when the suspected image is obtained via a CCTV camera.