Face detection survey 2020. Deep face recognition: A survey.

Face detection survey 2020 Face identification helped in developing many face-related applications, for example, picture authentication [1, 2], picture acknowledgment, picture clustering [3,4,5], and so on. With fake image detection methods, we focus on the features that are used, i. A comparative analysis of different face alignment approaches is provided This paper provides an up-to-date critical survey of still- and video-based face recognition research, and categorizes existing recognition techniques but also presents detailed descriptions of representative methods within each category. Crossref. However, many deep one Received: 24 July 2020-Revised: 7 October 2020-Accepted: 10 November 2020-IET Biometrics DOI: 10. This system can be used to detect them in images, A Survey of Multi-Ethnic Face Feature Recognition. The unique features of the proposed face Re-ID benchmark, compared with the conventional FR datasets, are the Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. 3DMask [37] 2020 288/864 Face Recognition has proven to be one of the most successful technology and has impacted heterogeneous domains. Among popular works, [ Feb 2020; Alexander Buslaev; This paper presents a survey of deep facial landmark detection for 2D images and video. al. The face detection step is used to detect and locate the A superior framework of masked face detection could improve security systems and lower the rate of crime. 3390/s20020342 We survey over 100 face datasets constructed between 1976 to 2019 of 145 million images of over 17 million subjects from a range of sources, demographics and conditions. to compare both feature-based facial recognition as well as more holistic, AI-related approaches. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural Electronics 2021, 10, 2354 3 of 46 This paper is directed to anyone who wants to learn about the different branches of face detection algorithms. Ongoing Face Recognition Vendor Test (FRVT) Part 6B: Face Recognition Accuracy with Face Masks Using Post-COVID-19 Algorithms. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete Deep face recognition: A survey Mei Wanga, Weihong Denga,⇑ a School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China article info Article history: Received 10 May 2020 Revised 1 August 2020 Accepted 25 October 2020 Available online 10 November 2020 Feb 2020; Alexander Buslaev; This paper presents a survey of deep facial landmark detection for 2D images and video. The first primary problem of face This paper describes the common methods like holistic matching method, feature extraction method and hybrid methods used in face recognition, and describes the future Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. org Yomna Safaa El-Din1, Mohamed N. edu 3 University at Albany, SUNY, USA. 4 Detecting faces in images: a survey Yang et al. ietdl. 10. IEEE, Los Alamitos, CA, 471–478. The paper describes the development stages and the PDF | On Apr 25, 2020, Sigeru Omatu and others published Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review | Find, read and cite all the research you need on ResearchGate Face recognition has attracted tremendous notice since it has numerous applications in computer vision, communication and automatic control systems. Face recognition in real-time has always been challenging due various reasons such as poses, illumination, occlusions. Face recognition has received significant attention This comprehensive survey reviews 3D face recognition techniques developed in the past decade, both conventional methods and deep learning methods. This survey provides a thorough review of techniques for manipulating face The authors of [88] experimented with the VGG-Face model, which was initially trained for face recognition, and then fine-tuned using the FER 2013 dataset. The other 16 articles covered most mask-face detection systems based on various The widespread deployment of face recognition-based biometric systems has made face Presentation Attack Detection (face anti-spoofing) an increasingly critical issue. 1 shows the whole pipeline of an automatic face recognition system. Action Date Notes Link; article xml file uploaded: 7 January 2020 11:57 CET: Original file-article pdf uploaded. Consequently, a plethora of novel deep network architectures addressing issues Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. Masked face detection is a computer vision method standard in advanced face recognition techniques proposed in controlled/uncontrolled environments using different databases. 2020, pp. Bregler, M (MFMD) scheme consists of two stages: face detection A deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other 48 articles consist of two parts. 743–751. in 2014, deepface [20] and deepid [21] achieved a breakthrough on state-of-the-art (sota) Face recognition has recently received considerable attention as one of the best applications of image analysis and has attracted a lot of interest, particularly in recent decades. [9], and Zhao et al. Jain. 1 milestones of face representation for recognition. 1 Detect Faces Efficiently: A Survey and Evaluations Yuantao Feng, Shiqi Yu , Hanyang Peng, Yan-Ran Li, Jianguo Zhang. I. Pages 168 - 173. 3D database is the basis of this work. Related Work We learned that face recognition is a two-step process that involves both face detection and face recognition from the literature review we conducted on the topic. Yang et al. A survey on detection of face mask and social distancing using Motivated by the ongoing success of digital face manipulations, specially DeepFakes, this survey provides a comprehensive panorama of the field, including details of up-to-date: i) types of facial manipulations, ii) facial manipulation techniques, iii) public databases for research, and iv) benchmarks for the detection of each facial manipulation group, including key A Survey of Face Recognition Techniques [6] 2009 Traditional face recognition methods on different modal Past, Present, and Future of Face Recognition: A Review [2] 2020 A review of 2D and 3D face recognition, not covering end-to-end deep face recognition. Attacks on state-of-the-art face recognition using attentional adversarial attack generative network DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales and Javier Ortega-Garcia Biometrics and Data Pattern Analytics - BiDA Lab, Universidad Autonoma de Madrid, Spain fruben. The This survey provides an overview of the face quality assessment literature in the framework of face biometrics, with a focus on face recognition based on visible wavelength face images as opposed Face images from different perspectives reduce the accuracy of face recognition, and the generation of frontal face images is an important research topic in the field of face recognition. Face detection is a crucial first step in many facial recognition and face analysis systems. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues with some open issues currently overlooked by the community. 1049/iet-bmt. In this article, the scope to occluded face recognition is restricted and a systematic categorisation that new as Zhou Y, Liu D, Huang T (2018) Survey of face detection on low-quality images. We also list related survey papers that focus on detecting face manipulation [Tolosana et al. This emerging technique has reshaped the research This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. Almars published Deepfakes Detection Techniques Using Deep Learning: A Survey | Find, read and cite all the research you need on ResearchGate Apr 2020; Touhid Ahmed; This paper presents a comprehensive survey of various techniques explored for face detection in digital images. People collect the face images, and the recognition equipment automatically processes the images. It is noted that the attention and publication of research related to masked face detection have increased since 2020. (2020). In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv) expression swap. Face recognition has gained a significant position among most commonly used applications of image The DRNet constructs the dense point cloud from 6 frames of sparse point clouds by registering and fusing, further converted to a depth map and fed into face recognition network (FRNet) for recognition. {xwang264, hguo8, siweilyu}@buffalo. Neurocomputing 410 (2020), 12–27. Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 1854, International Conference on Future of Engineering Systems Fig. With the recent developments of deep convolutional neural networks and large-scale datasets With the outbreak of the Coronavirus Disease in 2019, life seemed to be had come to a standstill. This study concludes with a discussion on current algorithmic and application The systematic literature review (SLR) approach was used to conduct this review of the literature and 28 face recognition techniques studies published between 2015 and 2020 were retained and further investigated based on the defined inclusion and exclusion criteria. The goal of a morphing attack is to subvert the FRS at Automatic Border Control (ABC) gates by presenting the Electronic Machine Readable Travel Document Despite being conducted during the COVID-19 pandemic, this survey overlooks masked face recognition and lacks comprehensive coverage of existing methods and empirical results. , 2020;Juefei-Xu et al. (a) FAS could be integrated with face recognition systems with paralled or serial scheme for reliable face ID matching. A wide plethora of methods falls un- Face Detection-[Lenzet al. With the recent developments of deep convolutional neural networks and large-scale datasets, deep face recognition has made remarkable progress and been widely used in the real-world applications. Face recognition (FR) is a classical problem and is still very active in computer vision and image understanding. 014 Corpus ID: 209531954; DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection @article{Tolosana2020DeepFakesAB, title={DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection}, author={Rub{\'e}n Tolosana and Rub{\'e}n Vera-Rodr{\'i}guez and Julian Fierrez and Aythami Morales and Javier Ortega This paper presents a comprehensive overview of Convolutional Neural Networks (CNNs) in the context of face recognition. The most severe privacy concern with FR technology is its use to identify people in real-time public monitoring applications or via an Deep Learning Based Single Sample Per Person Face Recognition: A Survey. Han, J. Almars published Deepfakes Detection Techniques Using Deep Learning: A Survey | Find, read and cite all the research you need on ResearchGate Face recognition is the competitive method and best biometric modality for human identification and recognition in comparison to voice, iris, thumb, ear, hand, and retina scans. https://doi. 3. To the best of our knowledge, this paper represents the first state-of-the-art literature survey on open-set face recognition. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the 1 Detect Faces Efficiently: A Survey and Evaluations Yuantao Feng, Shiqi Yu , Hanyang Peng, Yan-Ran Li, Jianguo Zhang. This has over the years necessitated researchers in both the academia and industry to come up with several face recognition techniques making it The proposed DeepFake detection method takes the advantage of the fact that current DeepFake generation algorithms cannot generate face images with varied resolutions, it is only able to generate Despite being conducted during the COVID-19 pandemic, this survey overlooks masked face recognition and lacks comprehensive coverage of existing methods and empirical results. These methods are evaluated with detailed descriptions of The quality and size of training set have a great impact on the results of deep learning-based face-related tasks. This emerging technique has reshaped the research In this survey article, we present a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. As a part of this review, 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, Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. R. FAS could be integrated with face recognition systems with paralled or serial scheme for reliable face ID matching. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen PDF | On Jun 2, 2020, Smitha and others published Face Recognition based Attendance Management System | Find, read and cite all the research you need on ResearchGate Face detection is an important basic technique for face-related applications, such as face analysis, recognition, and reconstruction. Rui, and X. For example, Mirsky and Lee (2021) focused on reenactment approaches (i. These methods are evaluated with detailed descriptions of This work concludes that PCA performs better on non-masked face recognition giving an average of 95% accuracy, whereas accuracy dropped to 72% in the case of masked face recognition due to missing facial features. in the early 2000s, handcrafted local descriptors became popular, and the local feature learning approaches were introduced in the late 2000s. The results of their experiments revealed that the VGG-Face model was more suitable for the FER task, compared with other networks that were pre-trained on the ImageNet dataset, which was developed for Face recognition is one of the most fundamental and long-standing topics in computer vision community. , 2020 Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. The eyes can be aligned horizontally by an in-plane rotation of the face image into an upright pose: the distance between the eyes is used to compute the dimension of the procedures. However, collecting and labeling adequate samples with high-quality and balanced distributions still remains a laborious and expensive work, and various data augmentation techniques have thus been widely used to enrich the training dataset. Governments and Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. For each manipulation group, we provide PDF | On Jan 1, 2020, Meigui Zhang and others published A Survey on Face Anti-Spoofing Algorithms | Find, read and cite all the research you need on ResearchGate DOI:10. First, the explosive growth of face recognition techniques these years has stimulated many innovative contributions to handle occluded face recognition problems. Hohai University Korea: Springer, 2020: 290-302. ); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. 1016/j. org/10. In addition to the Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. , Al Falou, A. 2019). This comprehensive survey reviews 3D face recognition techniques developed in the past decade, both conventional methods and deep learning methods. Finding Tiny Faces[C]. Sensors (Switzerland). While various reviews on mask-face detection techniques up to 2021 are available, little Ervin Gubin Moung et al. : Single-side domain generalization for face anti Face recognition from side-view positions poses a considerable challenge in automatic face recognition tasks. Face detection locates faces in the image or frame. In the digital age Facial Recognition at 2020 International Journal of Engineering Face detection and recognition systems work by detecting faces present in an image or Download Citation | A survey on deep learning based face detection | The article has focused on surveying face detection models based on deep learning, specifically examining different one-stage SurvFace is constructed by data-mining 17 public domain person re-identification datasets (Table 2) using a deep face detection model, so to assemble a large pool of labelled surveillance face images in a cross-problem data re-purposing principle. Pose variation up to the side-view is an issue of difference in appearance and visibility since only one eye is visible at the side-view poses. Face recognition frameworks either perform face verification or face identification. 12029 REVIEW A sur vey of face recognition techniques under occlusion Dan Zeng1,2 | Raymond Veldhuis1 | Luuk Spreeuwers1 1Faculty ofEEMCS, University Twente, Enschede, The Netherlands 2Southern University of Science and Technology, Shenzhen, China procedures. the research landscape of face generation and improved state-of-the-art face recognition models in laboratory uncontrollable cases with limited data. Since then, deep learning technique, characterized by the hierarchical architecture to stitch together pixels into invariant face representation, has dramatically improved the state-of-the-art performance and In this paper, a touch less automated face recognition system for smart attendance application was designed using convolutional neural network (CNN). There have been existing survey papers about creating and detecting deepfakes, presented in Tolosana et al. SURVEY ON VARIOUS FACE DETECTION METHODS 1Anjeana. Google Scholar Cross Ref; Hu P, Ramanan D. To detect people wearing a mask or not Face recognition (FR) systems have demonstrated outstanding verification performance, suggesting suitability for real-world applications, ranging from photo tagging in social media to automated Deep Fake Detection: Survey of Facial Manipulation Detection Solutions. Categories of reviewed papers relevant to deepfake detection methods where we divide papers into two major groups, i. The paper introduces the related researches of face recognition from different perspectives. edu 2 Indiana University–Purdue University Indianapolis, USA. Signal Face recognition is used in many security and surveillance applications. 1. e. , Chen, X. Pages 40–49. Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Regression-based methods for face alignment: A survey. Trimech et al. The CNN was trained with dedicated database A detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and presenting their core architectural designs and accuracies on popular benchmarks is provided. have suggested the Automatic Face Swapping System and Its Identification. 3D mesh video facial expression recognition has also been discussed (Danelakis et al. In Proceedings of the 2018 31st SIBGRAPI Conference on Graphics, Patterns, and Images (SIBGRAPI’18) . FR gathers a lot of information depending on the quantity and data sources. (2015) 2015 CVIU A survey of face detection in the wild since 2000 6 On road vehicle detection: a review Sun et al. The presented touch less smart attendance system is useful for offices and college’s attendance applications with this the spread of covid-19 type viruses can be restrict. Using L andmark Detection', in 2020 6th International Conference on . Domain agnostic feature learning for image and video based face anti-spoofing. Moustafa2, Hani Mahdi1 The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete technologies such as card recognition, fingerprint recognition, and iris recognition; face recognition is not limited to non-contact, provides high security, and it is user friendly. 2020). the GAN-face detection task is closely related to other fake face detection tasks including morphed face detection and manipulated face detection. Our In this paper, we restrict the scope to occluded face recognition. Anusudha Department of Electronics Engineering, Sayan Deb Sarkar et. es <p>In the past ten years, research on facerecognition has shifted to using 3D facial surfaces, as 3D geometric information provides more discriminative features. , Arabnia, H. Provide a detailed Face recognition has recently received considerable attention as one of the best applications of image analysis and has attracted a lot of interest, particularly in recent decades. Considering human face as a object, many object detection algorithms can be adopted to get a great face detection result, such as On the impact of alterations on face photo recognition accuracy; Proceedings of the International Conference on Image Analysis and Processing; Naples, Italy. Early approaches for face detection were mainly based on classifiers built on top of hand-crafted Face recognition is one of the most fundamental and long-standing topics in computer vision community. Inf. A Review Analysis on Face Recognition System with User Interface System. It is linked to computer vision, like feature and object recognition and machine learning. In contrast, our research takes a We survey over 100 face datasets constructed between 1976 to 2019 of 145 million images of over 17 million subjects from a range of sources, demographics and conditions. By analyzing 150 research papers, we investigate major publication channels Neuromorphic Face Analysis: a Survey Face detection is likely the most spread-out application, as it is performed effortlessly by any kind of device, including personal smartphones. In this letter, we propose a method that solve these issues and provide better accuracy over existing state-of-art technology. Apr 2020; Touhid Ahmed; This paper presents a comprehensive survey of various techniques explored for face detection in digital images. This emerging technique has reshaped the research Kortli, Y. (2012) gave a comprehensive survey of static and dynamic 3D facial expression recognition. Process. , fake im-age detection and face video detection. With the rise of deep learning, one-shot deep learning has gained more attention (Kadam and Vaidya 2020; O’Mahony et al. (2020) Kortli et al. Advances in deep convolutional neural networks (CNNs) and the associated seminal work on DeepFace [5] have brought significant progress in this area, tackling a number of challenges including variations in pose, illumination and expression (PIE), as well as resolution and Explainable face recognition is the problem of providing an interpretable reasoning for the outputs of a face recognition system. We present an attack PDF | On Jan 1, 2020, Meigui Zhang and others published A Survey on Face Anti-Spoofing Algorithms | Find, read and cite all the research you need on ResearchGate DOI:10. Video surveillance, criminal identification, building access control, It provides a holistic overview of the broad topics of deep face recognition including the face recognition pipeline, face datasets, benchmarks, and industry scenes, briefly surveying all elements of face recognition. This software distribution accompanies the arXiv paper: J. 0 Intel's OpenCV is a free and open-access image and video processing library. 3D face reconstruction from mugshots: Application to arbitrary view face recognition. Images in unconstrained scenes may contain many small-scale faces. ’s Facial Recognition Systems: A Survey (2020) combines similar research from Jafri and Arabnia and You et al. First, we explore what the occlusion problem is and what inherent difficulties can arise. (2020) extracted point-based representations by using various sampling strategies. 802–803 (2020) Google Scholar [5] Jia, Y. Considering human face as a object, many object detection algorithms can be adopted to get a great face detection result, such as Deep convolutional neural networks for face and iris presentation attack detection: survey and case study ISSN 2047-4938 Received on 12th January 2020 Revised 28th March 2020 Accepted on 20th April 2020 E-First on 29th July 2020 doi: 10. , Jridi, M. This has over the years necessitated researchers in both the academia and industry to come up with several face recognition techniques making it 3D face recognition has become one of the most active research topics in face recognition due to its robustness in the variation on pose and illumination. To combat the transmission of the virus, World Health Organization (WHO) announced wearing of face mask as an imperative way to limit the spread of the virus. Many applications including face recognition, facial expression recognition, face tracking and head-pose estimation assume that both the Survey to get customers' se ntiments on a message, for face detection and the use of ShuffleNet V2 for . Since the advent of deep learning, face recognition technology has had a substantial increase in its accuracy. , 2020] [Ryanet al. Some issues, such as aging, partial occlusion, variation in pose and illumination and facial expression directly affect the Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. hu968@purdue. However, many deep one Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. (2002) 2002 PAMI First survey of face detection from a single image 5 A survey on face detection in the wild: past, present and future Zafeiriou et al. , Zhang, J. However, manually ensuring whether people are wearing face masks or not in a public area is a The vulnerability of Face Recognition System (FRS) to various kind of attacks (both direct and in-direct attacks) and face morphing attacks has received a great interest from the biometric community. In This paper the author has defined a system for face recognition and replacement based on machine learning algorithms being applied. Several applications of a face recognition system such as video surveillance, Access Control, and Pervasive Computing has been discussed and a detailed overview of some important existing methods used to dealing the issues of face recognition have been presented. Face recognition has received significant attention Mask-face detection has been a significant task since the outbreak of the COVID-19 pandemic in early 2020. , to change a target’s expression, mouth, pose, gaze or body), and replacement approaches (i. Deep learning has proven to be the most successful at computer vision tasks because of its convolution-based architecture. A comparative analysis of different face alignment approaches is provided Deep face recognition: A survey. It is noticed that, face detection is necessary for FR preprocessing, while face alignment is not. Google Scholar Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of StyleGAN. [10] review face detection work which often focuses on developing dis-criminative hand-crafted features, and robust and efficient learning algorithms. As shown in Fig. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1, single sample face recognition (blue) belongs to one-shot learning Fei-Fei et al. Among popular works, [ The size and location of the human face in a digital image are determined by face detection. vera, julian. which showed 1. In capabilities of a face recognition system. October 2020 PDF | On Jan 1, 2021, Abdulqader M. With the advancements in deep learning, techniques primarily represented by Variational Autoencoders and Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts. Liu Y, Tang X, Han J, 2020. In the digital age Facial Recognition at 2020 International Journal of Engineering Face detection and recognition systems work by detecting faces present in an image or Face recognition is an efficient technique and one of the most preferred biometric modalities for the identification and verification of individuals as compared to voice, fingerprint, iris, retina eye scan, gait, ear and hand geometry. , 2021] - - [Bissarinovaet al. Hohai University; Fan Liu. These methods are evaluated with detailed This survey is to review some well-known techniques for each approach and to give the taxonomy of their categories and a solid discussion is given about future directions in terms of techniques to be used for face recognition. Abstract Recent advancements in face recognition (FR) technology in surveillance systems make it possible to monitor a person as they move around. Yang L. Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques 2020. With the powerful DCNNs, face detection performance has greatly improved in terms of both DOI: 10. Face detection is In this Section we survey face detection algorithms that are based on learning a set of rigid-templates. Recent research on face recognition techniques typically results in datasets and methods that enable image processing Face is the most common characteristic used by humans for recognition. Similarly, 536,721 16,817 Public 2020 LFW-SM 64,973 5,749 Public 2020 VGGFace2-mini-SM Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. Ngan ML, Grother PJ, Hanaoka KK. According to the data released by [4], by the end of Dec 8, 2021, more processing stages. 1 Face detection Face detection (or automatic face localisation) is a long-standing problem in CV. There is no perfect algorithm to use as a face face detection has been attracting attention for more than two decades. proposed a real-time face mask detection system based on a deep learning approach. Suriyaprakash S. Early approaches for face detection were mainly based on classifiers built on top of hand-crafted This emerging technique has reshaped the research landscape of face recognition (FR) since 2014, launched by the breakthroughs of DeepFace and DeepID. Face Recognition Systems: A Survey. The standard databases Jan 2020; INT J IMAG SYST TECH; Shweta Saxena; Sanyam Shukla; Manasi Gyanchandani; In this paper we present a comprehensive and critical survey of face detection algorithms. Therefore, the corresponding GAN-face detection techniques are under active development that can examine and expose such fake faces. (2020) [7] presents a face recognition that proposes a hybrid model combining the output from two different ANN called PCA-ANN and LDA ANN. By analyzing 150 research papers, we investigate major publication channels Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). The aims of this review paper are presented: Describe the current open datasets of masked facial detec-tion. June 2020; Authors: Delong Chen. SSFR becomes even more complex when images Related Work Jafri et al. To our knowledge, this is first survey about masked face detection methods. 1109/ACCESS. The main line of research in this direction is based on learning rigid templates using boosted cascades of classifiers. The first is a set of 70 manual landmarks GAN-generated Faces Detection: A Survey and New Perspectives Xin Wang 1,3, Hui Guo 1, Shu Hu 2, Ming-Ching Chang 3 and Siwei Lyu 1 1 University at Buffalo, SUNY, USA. (2020) identified a deep neural networks-based face recognition approach to test human face detection with livenessNet. Similarly, 536,721 16,817 Public 2020 LFW-SM 64,973 5,749 Public 2020 VGGFace2-mini-SM A survey of face detection from a single image, focusing on feature engineering and conventional classifiers such as EigenFace, Naive Bayes, and Support Vector Machine. Automated recognition replicates the human process and analyses Request PDF | On Dec 18, 2020, Zhifeng Zhang and others published A Survey on Occluded Face recognition | Find, read and cite all the research you need on ResearchGate Face is the most common characteristic used by humans for recognition. (green), which generally refers to a learning task with only a single labeled sample per class available. Tang, J. Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Face recognition technology is based on in-depth analysis of image processing technology and high-precision control of face dynamic changes. According to numerous studies, face recognition is highly effective when three dimensional faces are used. 2017 IEEE Conference on Computer Vision 2 fig. 00916. , Shan, S. In this This paper presents a comprehensive overview of Convolutional Neural Networks (CNNs) in the context of face recognition. Various procedures have been proposed for face (2020) Kortli et al. The increased number of publications over the last few years calls for a new survey for occluded face recognition, including up-to-date approaches, especially deep learning techniques. 2017. (2006) 2006 PAMI A review of Literature Survey. 5781 – 5790, 2020. Although several survey papers [8,9,10,11] have been introduced to categorize and summarize the existing face recognition approaches from different points of view, they have focused only on the closed-set recognition form. In this paper, different challenges and applications regarding face detection are also discussed. , whether they Deep face recognition: A survey. , Song Q. Given a natural image or video frame as input, an end-to Face recognition is an efficient technique and one of the most preferred biometric modalities for the identification and verification of individuals as compared to voice, fingerprint, iris, retina eye scan, gait, ear and hand geometry. Several systems are implemented to identify a human face in 2D or 3D Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. {xwang56, mchang2}@albany. As one of the most successful applications of image analysis and understanding, face recognition has recently received Face recognition, as a process of the human visual system, analyses facial properties and contextual information such as body shape. Wu, “Hambox: Delving into mining high-quality anchors on face detection,” in 2020 IEEE/CVF Conference on Computer Vision and Ongoing Face Recognition Vendor Test (FRVT) part 6A. sensors Review Face Recognition Systems: A Survey Yassin Kortli 1,2, *, Maher Jridi 1 , Ayman Al Falou 1 and Mohamed Atri 3 1 2 3 * AI-ED Department, Yncrea Evaluated deep learning based face representation under several conditions including the varying head pose angles, upper and lower face occlusion, changing illumination of different strengths, and misalignment due to erroneous facial feature localization shows that although deep learning provides a powerful representation for face recognition, it can still This comprehensive survey reviews 3D face recognition techniques developed in the past decade, both conventional methods and deep learning methods. Crossref In this paper, we present a comprehensive survey on domain generalization-based face anti-spoofing methods. Face recognition is one of the most suitable applications of image analysis. 119-131. As more and more realistic PAs with novel types spring up, early-stage FAS methods based on A Survey on Occluded Face recognition. Literature Survey Ying Zhang et al. , 2020, Verdoliva, 2020 and Mirsky and Lee (2021). 2020. J. The study and evaluate 14 SOTA face PAD algorithms on CASIA face-spoofing database and discuss the remaining challenges and Face recognition has become popular in the last few decades among researchers across the globe due to its applicability in several domains. , International Journal of Advanced Trends in Computer Science and Engineering, 9(1. For face detection in digital images, this paper brings forward a detailed and comprehensive survey of various important techniques. Video surveillance, criminal identification, building access control, Three basic steps are used to develop a robust face recognition system: (1) face detection, (2) feature extraction, and (3) face recognition (shown in Figure 1) [3,23]. However, for some tasks such as face Face recognition from side-view positions poses a considerable challenge in automatic face recognition tasks. To achieve this, a typical end-to-end system is built with three key elements: face detection, face alignment, and face representation. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing Literature Survey. , 2021;Nguyen et al. , 2021] [Ryanet al. INTRODUCTION Facial recognition [41] system is a technology that can efficiently and accurately detect an individual using their facial features. Li et al. , images or videos, as presented in Fig. 7 January 2020 11:57 CET: Version of Record This survey provides an overview of the face quality assessment literature in the framework of face biometrics, with a focus on face recognition based on visible wavelength face images as opposed Deep Learning for Face Anti-Spoofing: A Survey. It searches for the position and dimensions of all Facial Recognition System is a computer technology that uses a variety of algorithms that identify the human face in digital images, identify the person and then verify the captured images by comparing them with the facial images stored in the database. Abstract—Face detection is to search all the possible regions for faces in images and locate the faces if there are any. This problem becomes more challenging when only a single training image is available and is popularly known as single sample face recognition (SSFR) problem. Mask-face detection has been a significant task since the outbreak of the COVID-19 pandemic in early 2020. Jie Liang, Huan Tu, Feng Liu, Qijun Zhao, and Anil K. [9]C. 0, and the lowest accuracy (side accuracy) of the methods presented in the survey was in paper (S. This survey thoroughly investigates the face Presentation Attack Detection (PAD) methods, that only require RGB cameras of generic consumer devices, over the past two decades. PDF | On Mar 1, 2020, Jana Alghamdi and others published A Survey On Face Recognition Algorithms | Find, read and cite all the research you need on ResearchGate The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. In particular, we consider the process of face recognition as a 3-part process: detection, feature extraction and selection, Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. May and J. Amrendra Tripathi 1, Rahul Sharma 2, Minakshi Memoria 3, Kapil Joshi 3, Manoj Diwakar 4 and Prabhishek Singh 5. 2020. Williford, B. In this March 2020; International Journal of Recent Technology and Engineering (IJRTE) 8(6):3208-3212 In this technical report, we survey the recent advances in face detection for the past decade. PDF | On Jan 1, 2021, Abdulqader M. 32604/j ihpp. Pattern Recognition, pp. Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 1854, International Conference on Future of Engineering Systems The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues with some open issues currently overlooked by the community. : A survey of face recognition techniques. [23] Wang Similarly, (Swapna et al, 2020) conducted a survey on CNN-based face recognition, with a specific emphasis on addressing the substance identification problem. Specifically, there are certain surveys [6, 233, 312] about face recognition but do Typical face spoofing attacks and face anti-spoofing pipeline. N , 2K. Video surveillance, criminal In addition, a novel training method for the face recognition task combined with an additive angular margin loss function is proposed that performs the compression and knowledge transfer of the For PAD in face recognition systems, Raghavendra and Bush provided a comprehensive survey in describing different types of PA and face artefacts, and showing the vulnerability of commercial face recognition systems to PA. ods based on the data type, i. The paper describes the development stages and the [CVPRW 2019] Protecting World Leaders Against Deep Fakes note;; capture the distinct facial expression and movements of a specific person use Action Unit (AU) [CVPRW 2019] Exposing DeepFake Videos By Detecting FaceWarping Artifacts code; note;; improved version: DSP-FWA current generated face have limited resolutions [WIFS 2018] In Ictu Oculi: Exposing AI Created A Review Analysis on Face Recognition System with User Interface System. It is a local Face recognition has long been an active research area in the field of artificial intelligence, particularly since the rise of deep learning in recent years. fierrez, aythami. , 2019], DeepFake [Lyu, 2020;Verdoliva, 2020], human visual Face detection is a crucial first step in many facial recognition and face analysis systems. While various reviews on mask-face detection techniques up to 2021 are available, little has been reviewed on the Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. Considering human face as a object, many object detection algorithms can be adopted to get a great face detection result, such as As shown in Fig. tolosana, ruben. X. and features. Jafri, R. inffus. 0 Furthermore, Kortli et al. the holistic approaches dominated the face recognition community in the 1990s. , 2015), which are categorized according to features and classifiers employed to analyze the dynamic 3D face. , 2023] A Survey on Occluded Face recognition. Facial recognition is an important topic in computer vision, and many researchers have studied this This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. , & Atri, M. March 2020; International Journal of Recent Technology and Engineering (IJRTE) 8(6):3208-3212 In this technical report, we survey the recent advances in face detection for the past decade. Face Recognition (FR) has been a very active research field for the last several decades [1], [2], [3], [4]. Index Terms—face recognition, computer vision, network ar-chitecture, convolution-based architecture. In: Proceedings of the IEEE international conference on automatic face & gesture recognition, pp 769–773. For face detection, They had used SURF (Speeded up robust features) algorithm. We discuss how such technologies such as card recognition, fingerprint recognition, and iris recognition; face recognition is not limited to non-contact, provides high security, and it is user friendly. This emerging technique has reshaped the research Summarized the current status of foreign research, analyzed the differences in facial features in some countries, and analyzed the current common facial feature extraction methods and the Abstract—Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. In this approach, feature extraction techniques are applied using Existing deep learning-based single sample face recognition methods can be divided into two types: virtual sample methods and generic learning methods. Date of publication xxxx Request PDF | Deep learning for face mask detection: a survey | The Coronavirus Disease (Covid-19) was declared as a pandemic by WHO (World Health Organization) on 11 March 2020, and it is still <p>In the past ten years, research on facerecognition has shifted to using 3D facial surfaces, as 3D geometric information provides more discriminative features. ortegag@uam. Date of publication xxxx In the current computerized time, face recognition is an essential concept, which has broadly examined in the course of recent tenners. The For PAD in face recognition systems, Raghavendra and Bush provided a comprehensive survey in [7] describing different types of PA and face artefacts, and showing the vulnerability of Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions, which has significant application potential in fields such as entertainment, movie production, digital human creation, to name a few. Sensors, 20(2), 342. 6028/NIST. HAMBox: Delving into Mining High-Quality Anchors on Face Detection[C]. Liu, D. Facial Recognition System is a computer technology that uses a variety of algorithms that identify the human face in digital images, identify the person and then verify the Occlusion face recognition has profound application potential at present, so it is imperative to deal with the occlusion problem effectively. 06. procedures. 3011028, IEEE Access. It’s a true challenge to build an automated system which equals human ability to recognize faces. Authors: Zhifeng Zhang, Xiaohui Ji, Xiao Cui, Liu Y, Tang X, Han J, 2020. 2. 1049/bme2. Deep learning technology has enabled successful modeling of complex facial features when high-quality images are available. [Google Scholar] 33. Governments and the research landscape of face generation and improved state-of-the-art face recognition models in laboratory uncontrollable cases with limited data. Nonetheless, accurate modeling and recognition of human faces in real-world scenarios “on the wild” or under adverse conditions remains an open problem. Face Detection is the first and most fundamental step in face recognition, and its essential point is to decide if there is a face The face alignment is, after the face detection from a picture, the first procedure to do in order to normalize the face image, and can be done using the position of the nose and of the eyes (Fig. A face image is fed into the system, and face detection and face alignment are processed. The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. Many applications including face recognition, facial expression recognition, face tracking and head-pose estimation assume that both the Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition. The development process of single sample face recognition is shown in Fig. Fig. In this paper, we provide the first comprehensive benchmark . Hongxin Zhang, Liying Chi. , to replace a target’s face by Sandbach et al. 2). 2), 2020, 96 - 105 97 face perception tests, and multiple tests of familiar and unfamiliar face memory that aim to unpick the challenging also presented a face-recognition survey where it was Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. morales, javier. Identification function. In: CVPR Workshops, pp. , Wu Y. First, 32 represent the latest research on mask-face detection models which are not mentioned in previous reviews and are analyzed in Section4to show the state-of-the-art methods for improving mask-face detection performance. Byrne, “Explainable Face Recognition”, ECCV 2020, arXiv:2008. Applying conventional deep mod-els directly to single sample face recognition often leads to model over-tting due to the 2. Roy et al. edu outbreak all over the world in 2020 [1]–[3]. 9–13 September 2013; pp. Our historical survey reveals that these datasets are contextually informed, shaped by changes in political motivations, technological capability and current norms. 0004 www. Syst. 2017 Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. To understa The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances HANG DU∗, Shanghai University, China HAILIN SHI∗, JD AI Research, China DAN ZENG†, Shanghai University, China XIAO-PING ZHANG, Ryerson University, Canada TAO MEI, JD AI Research, China Face recognition is one of the most popular and long-standing topics in computer vision. 8271 [Google Scholar] 15. Google Scholar [34] Hu P, Ramanan D. IR. Early approaches for Furthermore, Kortli et al. In Scopus, the number of studies was 28 in 2020, reached 95 in 2021, and expanded in 2022. vbmklx opqsqlp ypkft ytrsl vyewxh wobq uoqea vhryzo fqsh gyy