# 3D Face Recognition: A Survey

arXiv: https://arxiv.org/abs/2108.11082 · HTML: https://mlsanity.com/paper/2108.11082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 148 references that are in the map.
- First posted on arXiv 25 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [Deep Learning Face Representation by Joint Identification-Verification](https://mlsanity.com/paper/1406.4773/) (2014)
- [DeepID3: Face Recognition with Very Deep Neural Networks](https://mlsanity.com/paper/1502.00873/) (2015)
- [Deeply learned face representations are sparse, selective, and robust](https://mlsanity.com/paper/1412.1265/) (2014)
- [Gaussian Process Morphable Models](https://mlsanity.com/paper/1603.07254/) (2016)
- [Learning from Millions of 3D Scans for Large-scale 3D Face Recognition](https://mlsanity.com/paper/1711.05942/) (2017)
- [Web-Scale Training for Face Identification](https://mlsanity.com/paper/1406.5266/) (2014)
- [3D Face Anti-spoofing with Factorized Bilinear Coding](https://mlsanity.com/paper/2005.06514/) (2020)
- [Deep 3D Face Identification](https://mlsanity.com/paper/1703.10714/) (2017)
- [Robust 3D face recognition in presence of pose and partial occlusions or missing parts](https://mlsanity.com/paper/1408.3709/) (2014)
- [Data-Free Point Cloud Network for 3D Face Recognition](https://mlsanity.com/paper/1911.04731/) (2019)
- [Face Recognition from Sequential Sparse 3D Data via Deep Registration](https://mlsanity.com/paper/1810.09658/) (2018)
- [Nasal Patches and Curves for Expression-robust 3D Face Recognition](https://mlsanity.com/paper/1901.00206/) (2019)

## Built on by (most cited first, 1 of 1)

- [DisguisOR: Holistic Face Anonymization for the Operating Room](https://mlsanity.com/paper/2307.14241/) (2023)

## Abstract (arXiv)

Face recognition is one of the most studied research topics in the community. In recent years, the research on face recognition has shifted to using 3D facial surfaces, as more discriminating features can be represented by the 3D geometric information. This survey focuses on reviewing the 3D face recognition techniques developed in the past ten years which are generally categorized into conventional methods and deep learning methods. The categorized techniques are evaluated using detailed descriptions of the representative works. The advantages and disadvantages of the techniques are summarized in terms of accuracy, complexity and robustness to face variation (expression, pose and occlusions, etc). The main contribution of this survey is that it comprehensively covers both conventional methods and deep learning methods on 3D face recognition. In addition, a review of available 3D face databases is provided, along with the discussion of future research challenges and directions.