# Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction

arXiv: https://arxiv.org/abs/1906.02429 · HTML: https://mlsanity.com/paper/1906.02429/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry](https://mlsanity.com/paper/2110.09772/) (2021)
- [Mask-invariant Face Recognition through Template-level Knowledge Distillation](https://mlsanity.com/paper/2112.05646/) (2021)
- [Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction](https://mlsanity.com/paper/2305.07269/) (2023)
- [Face recognition via compact second order image gradient orientations](https://mlsanity.com/paper/2201.09246/) (2022)
- [InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation](https://mlsanity.com/paper/2309.13516/) (2023)
- [State of the Art: Face Recognition](https://mlsanity.com/paper/2108.11821/) (2021)

## Abstract (arXiv)

In this paper, a very effective method to solve the contiguous face occlusion recognition problem is proposed. It utilizes the robust image gradient direction features together with a variety of mapping functions and adopts a hierarchical sparse and low-rank regression model. This model unites the sparse representation in dictionary learning and the low-rank representation on the error term that is usually messy in the gradient domain. We call it the "weak low-rankness" optimization problem, which can be efficiently solved by the framework of Alternating Direction Method of Multipliers (ADMM). The optimum of the error term has a similar weak low-rank structure as the reference error map and the recognition performance can be enhanced by leaps and bounds using weak low-rankness optimization. Extensive experiments are conducted on real-world disguise / occlusion data and synthesized contiguous occlusion data. These experiments show that the proposed gradient direction-based hierarchical adaptive sparse and low-rank (GD-HASLR) algorithm has the best performance compared to state-of-the-art methods, including popular convolutional neural network-based methods.