# Deep 3D Face Identification

arXiv: https://arxiv.org/abs/1703.10714 · HTML: https://mlsanity.com/paper/1703.10714/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2017.

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

- [Physical-World Optical Adversarial Attacks on 3D Face Recognition](https://mlsanity.com/paper/2205.13412/) (2022)
- [High-Accuracy RGB-D Face Recognition via Segmentation-Aware Face Depth Estimation and Mask-Guided Attention Network](https://mlsanity.com/paper/2112.11713/) (2021)
- [Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition](https://mlsanity.com/paper/2104.09145/) (2021)
- [Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution](https://mlsanity.com/paper/2211.13964/) (2022)
- [3D Face Recognition: A Survey](https://mlsanity.com/paper/2108.11082/) (2021)
- [DiFace: Cross-Modal Face Recognition through Controlled Diffusion](https://mlsanity.com/paper/2312.01367/) (2023)
- [2D-3D Attention and Entropy for Pose Robust 2D Facial Recognition](https://mlsanity.com/paper/2505.09073/) (2025)
- [Few-Data Guided Learning Upon End-to-End Point Cloud Network for 3D Face Recognition](https://mlsanity.com/paper/2103.16927/) (2021)

## Abstract (arXiv)

We propose a novel 3D face recognition algorithm using a deep convolutional neural network (DCNN) and a 3D augmentation technique. The performance of 2D face recognition algorithms has significantly increased by leveraging the representational power of deep neural networks and the use of large-scale labeled training data. As opposed to 2D face recognition, training discriminative deep features for 3D face recognition is very difficult due to the lack of large-scale 3D face datasets. In this paper, we show that transfer learning from a CNN trained on 2D face images can effectively work for 3D face recognition by fine-tuning the CNN with a relatively small number of 3D facial scans. We also propose a 3D face augmentation technique which synthesizes a number of different facial expressions from a single 3D face scan. Our proposed method shows excellent recognition results on Bosphorus, BU-3DFE, and 3D-TEC datasets, without using hand-crafted features. The 3D identification using our deep features also scales well for large databases.