mlsanityData to 13 Oct 2026

State of the Art: Face Recognition

arXiv 2108.11821 ↗First posted 26 Aug 2021cs.CVcited by 0

Not on the map. Why?

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 8 of its 40 references that are in the map. First posted on arXiv 26 Aug 2021.

Builds on

8 of 40 references.

References · 8

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
  2. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,838
  3. 2018Local Learning with Deep and Handcrafted Features for Facial Expression Recognitioncited by 23
  4. 2019Cross-Resolution Learning for Face Recognitioncited by 22
  5. 2016Robust LSTM-Autoencoders for Face De-Occlusion in the Wildcited by 17
  6. 2019Does Generative Face Completion Help Face Recognition?cited by 9
  7. 2019Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Directioncited by 6
  8. 2020Adversarial Attacks against Face Recognition: A Comprehensive Studycited by 5

Built on by

Not cited yet.