# Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance

arXiv: https://arxiv.org/abs/2209.15474 · HTML: https://mlsanity.com/paper/2209.15474/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [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)
- [Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking](https://mlsanity.com/paper/2006.06458/) (2020)
- [Robust Morph-Detection at Automated Border Control Gate using Deep Decomposed 3D Shape and Diffuse Reflectance](https://mlsanity.com/paper/1912.01372/) (2019)
- [Mutual Information Maximization on Disentangled Representations for Differential Morph Detection](https://mlsanity.com/paper/2012.01542/) (2020)
- [Differential Morph Face Detection using Discriminative Wavelet Sub-bands](https://mlsanity.com/paper/2106.13178/) (2021)
- [Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?](https://mlsanity.com/paper/2202.12426/) (2022)
- [Differential Morphed Face Detection Using Deep Siamese Networks](https://mlsanity.com/paper/2012.01541/) (2020)
- [Conditional Identity Disentanglement for Differential Face Morph Detection](https://mlsanity.com/paper/2107.02162/) (2021)

## Built on by (most cited first, 5 of 5)

- [Multispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study](https://mlsanity.com/paper/2304.03510/) (2023)
- [Robust Face Morphing Attack Detection Using Fusion of Multiple Features and Classification Techniques](https://mlsanity.com/paper/2305.03264/) (2023)
- [Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network](https://mlsanity.com/paper/2305.01294/) (2023)
- [Dealing with Subject Similarity in Differential Morphing Attack Detection](https://mlsanity.com/paper/2404.07667/) (2024)
- [Towards Zero-Shot Differential Morphing Attack Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2505.15332/) (2025)

## Abstract (arXiv)

Face Recognition Systems (FRS) are vulnerable to various attacks performed directly and indirectly. Among these attacks, face morphing attacks are highly potential in deceiving automatic FRS and human observers and indicate a severe security threat, especially in the border control scenario. This work presents a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD) algorithm based on the spherical interpolation and hierarchical fusion of deep features computed from six different pre-trained deep Convolutional Neural Networks (CNNs). Extensive experiments are carried out on the newly generated face morphing dataset (SCFace-Morph) based on the publicly available SCFace dataset by considering the real-life scenario of Automatic Border Control (ABC) gates. Experimental protocols are designed to benchmark the proposed and state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and capture distances. Obtained results have indicated the superior performance of the proposed D-MAD method compared to the existing methods.