# State of the Art: Face Recognition

arXiv: https://arxiv.org/abs/2108.11821 · HTML: https://mlsanity.com/paper/2108.11821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Local Learning with Deep and Handcrafted Features for Facial Expression Recognition](https://mlsanity.com/paper/1804.10892/) (2018)
- [Cross-Resolution Learning for Face Recognition](https://mlsanity.com/paper/1912.02851/) (2019)
- [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild](https://mlsanity.com/paper/1612.08534/) (2016)
- [Does Generative Face Completion Help Face Recognition?](https://mlsanity.com/paper/1906.02858/) (2019)
- [Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction](https://mlsanity.com/paper/1906.02429/) (2019)
- [Adversarial Attacks against Face Recognition: A Comprehensive Study](https://mlsanity.com/paper/2007.11709/) (2020)

## Abstract (arXiv)

Working with Child Sexual Exploitation Material (CSEM) in forensic applications might be benefited from the progress in automatic face recognition. However, discriminative parts of a face in CSEM, i.e., mostly the eyes, could be often occluded to difficult the victim's identification. Most of the face recognition approaches cannot deal with such kind of occlusions, resulting in inaccurate face recognition results. This document presents a short review face recognition methods for images with natural and eye occlude faces. The purpose is to select the best baseline approach for solving automatic face recognition of occluded faces.