# Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Information

arXiv: https://arxiv.org/abs/2110.13552 · HTML: https://mlsanity.com/paper/2110.13552/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Face Morphing Attack Generation & Detection: A Comprehensive Survey](https://mlsanity.com/paper/2011.02045/) (2020)
- [Deep Face Representations for Differential Morphing Attack Detection](https://mlsanity.com/paper/2001.01202/) (2020)
- [Reflection Analysis for Face Morphing Attack Detection](https://mlsanity.com/paper/1807.02030/) (2018)
- [Visualizing Object Detection Features](https://mlsanity.com/paper/1502.05461/) (2015)

## Built on by (most cited first, 8 of 8)

- [OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement](https://mlsanity.com/paper/2208.07841/) (2022)
- [Face Feature Visualisation of Single Morphing Attack Detection](https://mlsanity.com/paper/2304.13021/) (2023)
- [Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection](https://mlsanity.com/paper/2305.18216/) (2023)
- [Single Morphing Attack Detection using Siamese Network and Few-shot Learning](https://mlsanity.com/paper/2206.10969/) (2022)
- [Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications](https://mlsanity.com/paper/2408.09558/) (2024)
- [Impact of Synthetic Images on Morphing Attack Detection Using a Siamese Network](https://mlsanity.com/paper/2403.09380/) (2024)
- [Impact of Image Context for Single Deep Learning Face Morphing Attack Detection](https://mlsanity.com/paper/2309.00549/) (2023)
- [MorphGuard: Morph Specific Margin Loss for Enhancing Robustness to Face Morphing Attacks](https://mlsanity.com/paper/2505.10497/) (2025)

## Abstract (arXiv)

Face morphing attack detection is a challenging task. Automatic classification methods and manual inspection are realised in automatic border control gates to detect morphing attacks. Understanding how a machine learning system can detect morphed faces and the most relevant facial areas is crucial. Those relevant areas contain texture signals that allow us to separate the bona fide and the morph images. Also, it helps in the manual examination to detect a passport generated with morphed images. This paper explores features extracted from intensity, shape, texture, and proposes a feature selection stage based on the Mutual Information filter to select the most relevant and less redundant features. This selection allows us to reduce the workload and know the exact localisation of such areas to understand the morphing impact and create a robust classifier. The best results were obtained for the method based on Conditional Mutual Information and Shape features using only 500 features for FERET images and 800 features for FRGCv2 images from 1,048 features available. The eyes and nose are identified as the most critical areas to be analysed.