# Robust Morph-Detection at Automated Border Control Gate using Deep Decomposed 3D Shape and Diffuse Reflectance

arXiv: https://arxiv.org/abs/1912.01372 · HTML: https://mlsanity.com/paper/1912.01372/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 15 of 15)

- [Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?](https://mlsanity.com/paper/2202.12426/) (2022)
- [Morph Detection Enhanced by Structured Group Sparsity](https://mlsanity.com/paper/2111.14943/) (2021)
- [Algorithmic Fairness in Face Morphing Attack Detection](https://mlsanity.com/paper/2111.12115/) (2021)
- [Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance](https://mlsanity.com/paper/2209.15474/) (2022)
- [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)
- [Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting](https://mlsanity.com/paper/2210.15510/) (2022)
- [Recent Advancement in 3D Biometrics using Monocular Camera](https://mlsanity.com/paper/2401.02646/) (2024)
- [V-MAD: Video-based Morphing Attack Detection in Operational Scenarios](https://mlsanity.com/paper/2404.06963/) (2024)
- [In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems](https://mlsanity.com/paper/2507.15285/) (2025)
- [Find the Differences: Differential Morphing Attack Detection vs Face Recognition](https://mlsanity.com/paper/2604.14734/) (2026)
- [DifFoundMAD: Foundation Models meet Differential Morphing Attack Detection](https://mlsanity.com/paper/2604.17961/) (2026)
- [Enhancing Single-Image Facial Demorphing using Multimodal Large Language Models](https://mlsanity.com/paper/2605.25442/) (2026)

## Abstract (arXiv)

Face recognition is widely employed in Automated Border Control (ABC) gates, which verify the face image on passport or electronic Machine Readable Travel Document (eMTRD) against the captured image to confirm the identity of the passport holder. In this paper, we present a robust morph detection algorithm that is based on differential morph detection. The proposed method decomposes the bona fide image captured from the ABC gate and the digital face image extracted from the eMRTD into the diffuse reconstructed image and a quantized normal map. The extracted features are further used to learn a linear classifier (SVM) to detect a morphing attack based on the assessment of differences between the bona fide image from the ABC gate and the digital face image extracted from the passport. Owing to the availability of multiple cameras within an ABC gate, we extend the proposed method to fuse the classification scores to generate the final decision on morph-attack-detection. To validate our proposed algorithm, we create a morph attack database with overall 588 images, where bona fide are captured in an indoor lighting environment with a Canon DSLR Camera with one sample per subject and correspondingly images from ABC gates. We benchmark our proposed method with the existing state-of-the-art and can state that the new approach significantly outperforms previous approaches in the ABC gate scenario.