# Improving 2D face recognition via fine-level facial depth generation and RGB-D complementary feature learning

arXiv: https://arxiv.org/abs/2305.04426 · HTML: https://mlsanity.com/paper/2305.04426/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition](https://mlsanity.com/paper/1708.02734/) (2017)
- [High-Accuracy RGB-D Face Recognition via Segmentation-Aware Face Depth Estimation and Mask-Guided Attention Network](https://mlsanity.com/paper/2112.11713/) (2021)
- [Learning to Generate Facial Depth Maps](https://mlsanity.com/paper/1805.11927/) (2018)
- [Two-Level Attention-based Fusion Learning for RGB-D Face Recognition](https://mlsanity.com/paper/2003.00168/) (2020)

## Abstract (arXiv)

Face recognition in complex scenes suffers severe challenges coming from perturbations such as pose deformation, ill illumination, partial occlusion. Some methods utilize depth estimation to obtain depth corresponding to RGB to improve the accuracy of face recognition. However, the depth generated by them suffer from image blur, which introduces noise in subsequent RGB-D face recognition tasks. In addition, existing RGB-D face recognition methods are unable to fully extract complementary features. In this paper, we propose a fine-grained facial depth generation network and an improved multimodal complementary feature learning network. Extensive experiments on the Lock3DFace dataset and the IIIT-D dataset show that the proposed FFDGNet and I MCFLNet can improve the accuracy of RGB-D face recognition while achieving the state-of-the-art performance.