Detect Faces Efficiently: A Survey and Evaluations
- URL: http://arxiv.org/abs/2112.01787v1
- Date: Fri, 3 Dec 2021 08:39:40 GMT
- Title: Detect Faces Efficiently: A Survey and Evaluations
- Authors: Yuantao Feng, Shiqi Yu, Hanyang Peng, Yan-Ran Li, Jianguo Zhang
- Abstract summary: 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.
- Score: 13.105528567365281
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Face detection is to search all the possible regions for faces in images and
locate the faces if there are any. 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. In recent decades, researchers have created many typical and efficient
face detectors from the Viola-Jones face detector to current CNN-based ones.
However, with the tremendous increase in images and videos with variations in
face scale, appearance, expression, occlusion and pose, traditional face
detectors are challenged to detect various "in the wild" faces. The emergence
of 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. We further compare and
discuss the popular and challenging datasets and their evaluation metrics. A
comprehensive comparison of several successful deep learning-based face
detectors is conducted to uncover their efficiency using two metrics: FLOPs and
latency. The paper can guide to choose appropriate face detectors for different
applications and also to develop more efficient and accurate detectors.
Related papers
- 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) - 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) - A Comparative Study of Face Detection Algorithms for Masked Face
Detection [0.0]
A subclass of the face detection problem that has recently gained increasing attention is occluded face detection.
Three years on since the advent of the COVID-19 pandemic, there is still a complete lack of evidence regarding how well existing face detection algorithms perform on masked faces.
This article first offers a brief review of state-of-the-art face detectors and detectors made for the masked face problem, along with a review of the existing masked face datasets.
We evaluate and compare the performances of a well-representative set of face detectors at masked face detection and conclude with a discussion on the possible contributing factors to
arXiv Detail & Related papers (2023-05-18T16:03:37Z) - Robustness Disparities in Face Detection [64.71318433419636]
We present the first of its kind detailed benchmark of face detection systems, specifically examining the robustness to noise of commercial and academic models.
Across all the datasets and systems, we generally find that photos of individuals who are $textitmasculine presenting$, of $textitolder$, of $textitdarker skin type$, or have $textitdim lighting$ are more susceptible to errors than their counterparts in other identities.
arXiv Detail & Related papers (2022-11-29T05:22:47Z) - 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) - Psychophysical Evaluation of Human Performance in Detecting Digital Face
Image Manipulations [14.63266615325105]
This work introduces a web-based, remote visual discrimination experiment on the basis of principles adopted from the field of psychophysics.
We examine human proficiency in detecting different types of digitally manipulated face images, specifically face swapping, morphing, and retouching.
arXiv Detail & Related papers (2022-01-28T12:45:33Z) - Going Deeper Into Face Detection: A Survey [30.711114908611563]
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.
arXiv Detail & Related papers (2021-03-27T20:18:00Z) - 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) - 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.