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

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 using two different COTS FRS (COTS I Cognitec FaceVACS-SDK Version 9.4.2 and COTS II - Neurotechnology version 10.0) to quantify the vulnerability with ageing. Further, we also evaluate five different Morph Attack Detection (MAD) techniques to benchmark their detection performance with respect to ageing.

1. Introduction

Facial characteristics have been well explored for identifying and verifying individuals and numerous biometric systems have been deployed in operational applications for many years [21], [23]. The preference towards face based biometric systems is founded on multiple factors such as ease of capture of facial characteristic without invasive imaging, capturing at a stand-off distance both in semi-cooperative (voluntary identification/verification) and uncooperative scenarios (surveillance) [27], [5], [26]. While many of the breakthrough articles detailing iris and vein recognition systems have shown impeccable accuracy with very low false accepts and false rejects, those systems suffer from highly constrained image capturing processes. In order to reach the performance of such iris and vein recognition systems, face biometrics has seen benefits from recent algorithmic advancements, which was focused on features that have been engineered in a robust manner [31], [30], [6], and pre-processing that has been improvised [35] by including end-to-end learning using Deep Neural Networks (DNN) even in large scale applications [26], [39]. Figure 1:

Illustration of the influence of ageing on face morphing