# 3D Face Reconstruction: the Road to Forensics

arXiv: https://arxiv.org/abs/2309.11357 · HTML: https://mlsanity.com/paper/2309.11357/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 240 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [3D Morphable Face Models -- Past, Present and Future](https://mlsanity.com/paper/1909.01815/) (2019)
- [Towards Fast, Accurate and Stable 3D Dense Face Alignment](https://mlsanity.com/paper/2009.09960/) (2020)
- [Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision](https://mlsanity.com/paper/1905.06817/) (2019)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks](https://mlsanity.com/paper/2003.05653/) (2020)
- [Towards Large-Pose Face Frontalization in the Wild](https://mlsanity.com/paper/1704.06244/) (2017)
- [Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications](https://mlsanity.com/paper/1606.03237/) (2016)
- [Biometric Quality: Review and Application to Face Recognition with FaceQnet](https://mlsanity.com/paper/2006.03298/) (2020)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images](https://mlsanity.com/paper/2003.08124/) (2020)
- [Effective Face Frontalization in Unconstrained Images](https://mlsanity.com/paper/1411.7964/) (2014)
- [Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation](https://mlsanity.com/paper/2205.03962/) (2022)
- [Towards Interpretable Face Recognition](https://mlsanity.com/paper/1805.00611/) (2018)
- [Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition](https://mlsanity.com/paper/1803.11366/) (2018)
- [On the use of automatically generated synthetic image datasets for benchmarking face recognition](https://mlsanity.com/paper/2106.04215/) (2021)
- [Visualizing Deep Similarity Networks](https://mlsanity.com/paper/1901.00536/) (2019)
- [Survey on 3D face reconstruction from uncalibrated images](https://mlsanity.com/paper/2011.05740/) (2020)
- [Face Recognition in Low Quality Images: A Survey](https://mlsanity.com/paper/1805.11519/) (2018)
- [Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection](https://mlsanity.com/paper/2106.07852/) (2021)
- [How are attributes expressed in face DCNNs?](https://mlsanity.com/paper/1910.05657/) (2019)
- [Sparse Photometric 3D Face Reconstruction Guided by Morphable Models](https://mlsanity.com/paper/1711.10870/) (2017)
- [3D Face Reconstruction for Forensic Recognition -- A Survey](https://mlsanity.com/paper/2303.11164/) (2023)

## Built on by (most cited first, 7 of 7)

- [Deepfake Media Forensics: State of the Art and Challenges Ahead](https://mlsanity.com/paper/2408.00388/) (2024)
- [Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance](https://mlsanity.com/paper/2504.18886/) (2025)
- [Exploring 3D Face Reconstruction and Fusion Methods for Face Verification: A Case-Study in Video Surveillance](https://mlsanity.com/paper/2409.10481/) (2024)
- [Tex-ViT: A Generalizable, Robust, Texture-based dual-branch cross-attention deepfake detector](https://mlsanity.com/paper/2408.16892/) (2024)
- [Fragile Watermarking for Image Certification Using Deep Steganographic Embedding](https://mlsanity.com/paper/2504.13759/) (2025)
- [Deep Data Hiding for ICAO-Compliant Face Images: A Survey](https://mlsanity.com/paper/2508.19324/) (2025)
- [Deceptive Beauty: Evaluating the Impact of Beauty Filters on Deepfake and Morphing Attack Detection](https://mlsanity.com/paper/2509.14120/) (2025)

## Abstract (arXiv)

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them.