On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection

TL;DR

This study investigates how aging affects the vulnerability of face recognition systems to morphing attacks, introducing a new dataset and evaluation metric, and benchmarking detection techniques under aging conditions.

Contribution

The paper presents a new face morphing dataset with aging effects, a novel vulnerability metric (FMMPMR), and evaluates multiple face recognition and attack detection methods considering aging.

Findings

Aging increases vulnerability of face recognition systems to morphing attacks.

The FMMPMR metric effectively quantifies attack vulnerability under aging.

Detection performance varies significantly with age intervals.

Abstract

Face morphing attacks have raised critical concerns as they demonstrate a new vulnerability of Face Recognition Systems (FRS), which are widely deployed in border control applications. The face morphing process uses the images from multiple data subjects and performs an image blending operation to generate a morphed image of high quality. The generated morphed image exhibits similar visual characteristics corresponding to the biometric characteristics of the data subjects that contributed to the composite image and thus making it difficult for both humans and FRS, to detect such attacks. In this paper, we report a systematic investigation on the vulnerability of the Commercial-Off-The-Shelf (COTS) FRS when morphed images under the influence of ageing are presented. To this extent, we have introduced a new morphed face dataset with ageing derived from the publicly available MORPH II face dataset, which we refer to as MorphAge dataset. The dataset has two bins based on age intervals, the first bin - MorphAge-I dataset has 1002 unique data subjects with the age variation of 1 year to 2 years while the MorphAge-II dataset consists of 516 data subjects whose age intervals are from 2 years to 5 years. To effectively evaluate the vulnerability for morphing attacks, we also introduce a new evaluation metric, namely the Fully Mated Morphed Presentation Match Rate (FMMPMR), to quantify the vulnerability effectively in a realistic scenario. Extensive experiments are carried out by using two different COTS FRS (COTS I - Cognitec and COTS II - Neurotechnology) to quantify the vulnerability with ageing. Further, we also evaluate five different Morph Attack Detection (MAD) techniques to benchmark their detection performance with ageing.