Longitudinal Analysis of Mask and No-Mask on Child Face Recognition

TL;DR

This study investigates how face masks and aging affect child face recognition over time, revealing significant accuracy declines in top systems, especially with masks and aging combined.

Contribution

It provides a comprehensive longitudinal analysis of child face recognition under mask and aging effects, using synthetic masks and multiple face matchers on a large dataset.

Findings

Face verification accuracy drops by up to 25% with masks and aging.

Mask and aging effects significantly impair child face recognition systems.

Top systems show varying degrees of robustness to mask and age variations.

Abstract

Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems (FRS). However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on FRS. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-the-shelf (COTS) system under child cross-age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended Indian Child Longitudinal Face Dataset) that contains 26,258 face images of 7,473 subjects in the age group of [2, 18] over an average time span of 3.35 years. Due to the combined effects of face mask and face aging, the FaceNet, PFE, ArcFace, and COTS face verification system accuracies decrease approximately 25%, 22%, 18%, 12%, respectively.