State of the Art: Face Recognition

• URL: http://arxiv.org/abs/2108.11821v1
• Date: Thu, 26 Aug 2021 14:37:29 GMT
• Title: State of the Art: Face Recognition
• Authors: Rubel Biswas and Pablo Blanco-Medina
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• CLIP Unreasonable Potential in Single-Shot Face Recognition [0.0]
  Face recognition is a core task in computer vision designed to identify and authenticate individuals by analyzing facial patterns and features. Recent Contrastive Language Image Pretraining (CLIP) a model developed by OpenAI has shown promising advancements. CLIP links natural language processing with vision tasks allowing it to generalize across modalities.
  arXiv  Detail & Related papers  (2024-11-19T08:23:52Z)
• TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks [7.092869001331781]
  Face recognition systems are widely deployed in high-security applications. Digital manipulations, such as face morphing, pose a security threat to face recognition systems. We present a novel method for adapting deep learning-based face recognition systems to be more robust against face morphing attacks.
  arXiv  Detail & Related papers  (2024-01-21T21:04:05Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• FACE-AUDITOR: Data Auditing in Facial Recognition Systems [24.082527732931677]
  Few-shot-based facial recognition systems have gained increasing attention due to their scalability and ability to work with a few face images. To prevent the face images from being misused, one straightforward approach is to modify the raw face images before sharing them. We propose a complete toolkit FACE-AUDITOR that can query the few-shot-based facial recognition model and determine whether any of a user's face images is used in training the model.
  arXiv  Detail & Related papers  (2023-04-05T23:03:54Z)
• A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios [0.0]
  This study presents comparative benchmark tables for the state-of-art face recognition methods. We constructed a video surveillance dataset of face IDs with high age variance, intra-class variance (face make-up, beard, etc.) with native surveillance facial imagery data for evaluation. On the other hand, this work discovers the best recognition methods for different conditions like non-masked faces, masked faces, and faces with glasses.
  arXiv  Detail & Related papers  (2022-11-05T17:59:18Z)
• End2End Occluded Face Recognition by Masking Corrupted Features [82.27588990277192]
  State-of-the-art general face recognition models do not generalize well to occluded face images. This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks.
  arXiv  Detail & Related papers  (2021-08-21T09:08:41Z)
• SynFace: Face Recognition with Synthetic Data [83.15838126703719]
  We devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the performance gap. We also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
  arXiv  Detail & Related papers  (2021-08-18T03:41:54Z)
• Harnessing Unrecognizable Faces for Face Recognition [87.80037162457427]
  We propose a measure of recognizability of a face image, implemented by a deep neural network trained using mostly recognizable identities. We show that accounting for recognizability reduces error rate of single-image face recognition by 58% at FAR=1e-5.
  arXiv  Detail & Related papers  (2021-06-08T05:25:03Z)
• Facial Expressions as a Vulnerability in Face Recognition [73.85525896663371]
  This work explores facial expression bias as a security vulnerability of face recognition systems. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies.
  arXiv  Detail & Related papers  (2020-11-17T18:12:41Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.