Going Deeper Into Face Detection: A Survey

• URL: http://arxiv.org/abs/2103.14983v1
• Date: Sat, 27 Mar 2021 20:18:00 GMT
• Title: Going Deeper Into Face Detection: A Survey
• Authors: Shervin Minaee, Ping Luo, Zhe Lin, Kevin Bowyer
• Abstract summary: Face detection is a crucial first step in many facial recognition and face analysis systems. With the breakthrough work in image classification using deep neural networks in 2012, there has been a huge paradigm shift in face detection. In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods.
• Score: 30.711114908611563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face detection is a crucial first step in many facial recognition and face analysis systems. Early approaches for face detection were mainly based on classifiers built on top of hand-crafted features extracted from local image regions, such as Haar Cascades and Histogram of Oriented Gradients. However, these approaches were not powerful enough to achieve a high accuracy on images of from uncontrolled environments. With the breakthrough work in image classification using deep neural networks in 2012, there has been a huge paradigm shift in face detection. Inspired by the rapid progress of deep learning in computer vision, many deep learning based frameworks have been proposed for face detection over the past few years, achieving significant improvements in accuracy. In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and present their core architectural designs and accuracies on popular benchmarks. We also describe some of the most popular face detection datasets. Finally, we discuss some current challenges in the field, and suggest potential future research directions.

Related papers

• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method [77.65459419417533]
  We put face forgery in a semantic context and define that computational methods that alter semantic face attributes are sources of face forgery. We construct a large face forgery image dataset, where each image is associated with a set of labels organized in a hierarchical graph. We propose a semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task.
  arXiv  Detail & Related papers  (2024-05-14T10:24:19Z)
• LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition [37.4550614524874]
  We focus on learning facial representations that can be adapted to train effective face recognition models. We explore the learning strategy of unlabeled facial images through self-supervised pretraining. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks.
  arXiv  Detail & Related papers  (2024-03-13T01:07:55Z)
• DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection [67.3143177137102]
  Deepfake detection refers to detecting artificially generated or edited faces in images or videos. We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
  arXiv  Detail & Related papers  (2023-12-07T07:19:45Z)
• 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)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• 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)
• Detect Faces Efficiently: A Survey and Evaluations [13.105528567365281]
  Many applications including face recognition, facial expression recognition, face tracking and head-pose estimation assume that both the location and the size of faces are known in the image. Deep learning techniques brought remarkable breakthroughs to face detection along with the price of a considerable increase in computation. This paper introduces representative deep learning-based methods and presents a deep and thorough analysis in terms of accuracy and efficiency.
  arXiv  Detail & Related papers  (2021-12-03T08:39:40Z)
• The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances [56.432660252331495]
  Face recognition is one of the most popular and long-standing topics in computer vision. Deep face recognition has made remarkable progress and been widely used in many real-world applications. In this survey article, we present a comprehensive review about the recent advance of each element.
  arXiv  Detail & Related papers  (2020-09-28T13:02:17Z)

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.