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

arXiv: https://arxiv.org/abs/2007.02684 · HTML: https://mlsanity.com/paper/2007.02684/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Morphing Attack Potential](https://mlsanity.com/paper/2204.13374/) (2022)
- [Deep Composite Face Image Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2211.11039/) (2022)
- [Algorithmic Fairness in Face Morphing Attack Detection](https://mlsanity.com/paper/2111.12115/) (2021)
- [Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection](https://mlsanity.com/paper/2305.18216/) (2023)
- [Worst-Case Morphs: a Theoretical and a Practical Approach](https://mlsanity.com/paper/2111.15416/) (2021)
- [Robust Face Morphing Attack Detection Using Fusion of Multiple Features and Classification Techniques](https://mlsanity.com/paper/2305.03264/) (2023)
- [Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN](https://mlsanity.com/paper/2310.08371/) (2023)
- [TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks](https://mlsanity.com/paper/2401.11598/) (2024)
- [DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing](https://mlsanity.com/paper/2507.17158/) (2025)
- [Face Morphing: Fooling a Face Recognition System Is Simple!](https://mlsanity.com/paper/2205.13796/) (2022)

## Abstract (arXiv)

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.