  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.
