# Face Recognition: A Novel Multi-Level Taxonomy based Survey

arXiv: https://arxiv.org/abs/1901.00713 · HTML: https://mlsanity.com/paper/1901.00713/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

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

- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [CapsField: Light Field-based Face and Expression Recognition in the Wild using Capsule Routing](https://mlsanity.com/paper/2101.03503/) (2021)
- [Depth as Attention for Face Representation Learning](https://mlsanity.com/paper/2101.00652/) (2021)
- [Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis](https://mlsanity.com/paper/2403.06529/) (2024)
- [GaitPoint+: A Gait Recognition Network Incorporating Point Cloud Analysis and Recycling](https://mlsanity.com/paper/2404.10213/) (2024)
- [Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition](https://mlsanity.com/paper/2501.16608/) (2025)
- [Adaptive Calibration for Fair and Performant Facial Recognition](https://mlsanity.com/paper/2606.04469/) (2026)

## Abstract (arXiv)

In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. To help understanding the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison of the existing solutions. This paper proposes a new, more encompassing and richer multi-level face recognition taxonomy, facilitating the organization and categorization of available and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support and feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. The paper concludes with a discussion on current algorithmic and application related challenges which may define future research directions for face recognition.