Relevant bibliographies by topics / Video - Face Detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Video - Face Detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Video - Face Detection"

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Bhuvaneshwari, T., N. Ramadevi, and E. Kalpana. "Face Quality Detection in a Video Frame." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 2206–11. http://dx.doi.org/10.22214/ijraset.2023.55559.

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Abstract: Face detection technology is often used for surveillance of detecting and tracking of people in real time. The applications using these algorithms deal with low quality video feeds having less Pixels Per Inch (ppi) and/or low frame rate. The algorithms perform well with such video feeds, but their performance deteriorates towards high quality, high data-per-frame videos. This project focuses on developing such an algorithm that gives faster results on high quality videos, at par with the algorithms working on low quality videos. The proposed algorithm uses MTCNN as base algorithm, an
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Agrawal, Priyanka. "Smart Surveillance System using Face Tracking." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2613–17. http://dx.doi.org/10.22214/ijraset.2021.35567.

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The face is seen as a key component of the human body, and humans utilise it to identify one another. Face detection in video refers to the process of detecting a person's face from a video sequence, while face tracking refers to the process of tracking the person's face throughout the video. Face detection and tracking has become a widely researched issue due to applications such as video surveillance systems and identifying criminal activity. However, working with videos is tough due to problems such as bad illumination, low resolution, and atypical posture, among others. It is critical to p
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Ponnapalli, Bharath, Gangadhara Sai Kutukuppala, Sandeep Monavarthi, Sri Vachan Goli, and B. Rajesh. "Monitoring of Video Surveillance." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 983–88. http://dx.doi.org/10.22214/ijraset.2023.50212.

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Abstract: The system gathers a sizable dataset of human faces during the data gathering phase in order to train the machine learning algorithms. In the face detection phase, computer vision algorithms are used to discover and recognise human faces in an image or video stream. You may accomplish this by using The deep convolutional neural network (CNN) architecture of the VGG16 algorithm is one of the most widely used algorithms for this job. The VGG16 algorithm has produced cutting-edge outcomes in a variety of computer vision applications, such as face recognition. Computer vision and machine
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Ismail, Aya, Marwa Elpeltagy, Mervat Zaki, and Kamal A. ElDahshan. "Deepfake video detection: YOLO-Face convolution recurrent approach." PeerJ Computer Science 7 (September 21, 2021): e730. http://dx.doi.org/10.7717/peerj-cs.730.

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Recently, the deepfake techniques for swapping faces have been spreading, allowing easy creation of hyper-realistic fake videos. Detecting the authenticity of a video has become increasingly critical because of the potential negative impact on the world. Here, a new project is introduced; You Only Look Once Convolution Recurrent Neural Networks (YOLO-CRNNs), to detect deepfake videos. The YOLO-Face detector detects face regions from each frame in the video, whereas a fine-tuned EfficientNet-B5 is used to extract the spatial features of these faces. These features are fed as a batch of input se
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P.Dahake, R., and M. U. Kharat. "Face Detection and Processing: a Survey." International Journal of Engineering & Technology 7, no. 4.19 (2018): 1066. http://dx.doi.org/10.14419/ijet.v7i4.19.28287.

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In the recent era facial image processing is gaining more importance and the face detection from image or from video have number of applications which are video surveillance, entertainment, security, multimedia, communication, Ubiquitous computing etc. Various research work are carried out for face detection and processing which includes detection, tracking of the face, estimation of pose, clustering the detected faces etc. Although significant advances have been made, the performance of face detection systems provide satisfactory under controlled environment & may get degraded with some c
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Shahar, Hadas, and Hagit Hel-Or. "Fake Video Detection Using Facial Color." Color and Imaging Conference 2020, no. 28 (2020): 175–80. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.27.

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The field of image forgery is widely studied, and with the recent introduction of deep networks based image synthesis, detection of fake image sequences has increased the challenge. Specifically, detecting spoofing attacks is of grave importance. In this study we exploit the minute changes in facial color of human faces in videos to determine real from fake videos. Even when idle, human skin color changes with sub-dermal blood flow, these changes are enhanced under stress and emotion. We show that extracting facial color along a video sequence can serve as a feature for training deep neural ne
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Megawan, Sunario, Wulan Sri Lestari, and Apriyanto Halim. "Deteksi Non-Spoofing Wajah pada Video secara Real Time Menggunakan Faster R-CNN." Journal of Information System Research (JOSH) 3, no. 3 (2022): 291–99. http://dx.doi.org/10.47065/josh.v3i3.1519.

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Face non-spoofing detection is an important job used to ensure authentication security by performing an analysis of the captured faces. Face spoofing is the process of fake faces by other people to gain illegal access to the biometric system which can be done by displaying videos or images of someone's face on the monitor screen or using printed images. There are various forms of attacks that can be carried out on the face authentication system in the form of face sketches, face photos, face videos and 3D face masks. Such attacks can occur because photos and videos of faces from users of the f
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Wakchaure, Shraddha, Avanti Tambe, Pratik Gadhave, Shubham Sandanshiv, and Mrs Archana Kadam. "Smart Exam Proctoring System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4507–10. http://dx.doi.org/10.22214/ijraset.2023.51358.

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Abstract: As the world is shifting towards digitalization, mostof the exams and assessments are being conducted online. These exams must be proctored. Several students are accessing thetest at the same time. It is very difficult to manually look if a student is committing malpractice. This project aims to use face detection and recognition for proctoring exams. Face detectionis the process of detecting faces in a video or image while face recognition is identifying or verifying a face from images orvideos. There are several research studies done on the detectionand recognition of faces owing t
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Hubálovský, Štěpán, Pavel Trojovský, Nebojsa Bacanin, and Venkatachalam K. "Evaluation of deepfake detection using YOLO with local binary pattern histogram." PeerJ Computer Science 8 (September 13, 2022): e1086. http://dx.doi.org/10.7717/peerj-cs.1086.

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Recently, deepfake technology has become a popularly used technique for swapping faces in images or videos that create forged data to mislead society. Detecting the originality of the video is a critical process due to the negative pattern of the image. In the detection of forged images or videos, various image processing techniques were implemented. Existing methods are ineffective in detecting new threats or false images. This article has proposed You Only Look Once–Local Binary Pattern Histogram (YOLO-LBPH) to detect fake videos. YOLO is used to detect the face in an image or a frame of a v
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PHAM-NGOC, PHUONG-TRINH, TAE-HO KIM, and KANG-HYUN JO. "ROBUST FACE DETECTION FOR MOVING PICTURES UNDER POSE, ROTATION, ILLUMINATION AND OCCLUSION CHANGES." International Journal of Information Acquisition 04, no. 04 (2007): 291–302. http://dx.doi.org/10.1142/s0219878907001368.

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Face detection has been a key step in face analysis systems for decades. However, it is still a challenging task due to the variation in image background, view, pose, occlusion, etc. This paper proposes a simple and effective tool to detect human faces in moving pictures under such conditions. An improved approach aiming to reduce impacts of illumination, scale and connection of faces to receive rapidly skin homogeneous regions considered as the most potential face candidates is presented. A hybrid classifier, applied in retrieved face candidates, is based on template matching and appearance-b
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Dissertations / Theses on the topic "Video - Face Detection"

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LI, Songyu. "A New Hands-free Face to Face Video Communication Method : Profile based frontal face video reconstruction." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-152457.

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This thesis proposes a method to reconstruct a frontal facial video basedon encoding done with the facial profile of another video sequence.The reconstructed facial video will have the similar facial expressionchanges as the changes in the profile video. First, the profiles for boththe reference video and for the test video are captured by edge detection.Then, asymmetrical principal component analysis is used to model thecorrespondence between the profile and the frontal face. This allows en-coding from a profile and decoding of the frontal face of another video.Another solution is to use dyna
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Tsishkou, Dzmitry. "Face detection, matching and recognition for semantic video understanding." Ecully, Ecole centrale de Lyon, 2005. http://www.theses.fr/2005ECDL0044.

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The objective of this work can be summarized as follows : to propose face detection and recognition in video solution that is enough fast, accurate and reliable to be implemented in the semantic video understanding system that is capable of replacing human expert in a variety of multimedia indexing applications. Meanwhile we assume that the research results that were raised during this work are complete enough to be adapted or modified as a part of other image processing, pattern recognition and video indexing and analysis systems.
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Hadid, A. (Abdenour). "Learning and recognizing faces: from still images to video sequences." Doctoral thesis, University of Oulu, 2005. http://urn.fi/urn:isbn:9514277597.

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Abstract Automatic face recognition is a challenging problem which has received much attention during recent years due to its many applications in different fields such as law enforcement, security applications, human-machine interaction etc. Up to date there is no technique that provides a robust solution for all situations and different applications. From still gray images to face sequences (and passing through color images), this thesis provides new algorithms to learn, detect and recognize faces. It also analyzes some emerging directions such as the integration of facial dynamics in the r
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Bhattarai, Smrity. "Digital Architecture for real-time face detection for deep video packet inspection systems." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1492787219112947.

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Papageorgiou, Constantine P. "A Trainable System for Object Detection in Images and Video Sequences." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/5566.

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This thesis presents a general, trainable system for object detection in static images and video sequences. The core system finds a certain class of objects in static images of completely unconstrained, cluttered scenes without using motion, tracking, or handcrafted models and without making any assumptions on the scene structure or the number of objects in the scene. The system uses a set of training data of positive and negative example images as input, transforms the pixel images to a Haar wavelet representation, and uses a support vector machine classifier to learn the differe
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Chenaoua, Kamal S. "Automatic detection of human skin in two-dimensional and complex imagery." Thesis, Queen's University Belfast, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680864.

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Doyle, Jason Emory. "Automatic Dynamic Tracking of Horse Head Facial Features in Video Using Image Processing Techniques." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87582.

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The wellbeing of horses is very important to their care takers, trainers, veterinarians, and owners. This thesis describes the development of a non-invasive image processing technique that allows for automatic detection and tracking of horse head and ear motion, respectively, in videos or camera feed, both of which may provide indications of horse pain, stress, or well-being. The algorithm developed here can automatically detect and track head motion and ear motion, respectively, in videos of a standing horse. Results demonstrating the technique for nine different horses are presented, where t
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Kramer, Annika. "Model based methods for locating, enhancing and recognising low resolution objects in video." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/585.

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Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most computer vision algorithms struggle with wide angle surveillance videos that make automatic processing difficult due to low resolution and poor detail objects. Additional problems arise from varying pose and lighting conditions as well as non-cooperative subjects. All these constraints pose problems for automatic scene interpretation of surveillance video, including object dete
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Malla, Amol Man. "Automated video-based measurement of eye closure using a remote camera for detecting drowsiness and behavioural microsleeps." Thesis, University of Canterbury. Electrical and Computer Engineering, 2008. http://hdl.handle.net/10092/2111.

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A device capable of continuously monitoring an individual’s levels of alertness in real-time is highly desirable for preventing drowsiness and lapse related accidents. This thesis presents the development of a non-intrusive and light-insensitive video-based system that uses computer-vision methods to localize face, eyes, and eyelids positions to measure level of eye closure within an image, which, in turn, can be used to identify visible facial signs associated with drowsiness and behavioural microsleeps. The system was developed to be non-intrusive and light-insensitive to make it practical
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Juráček, Aleš. "Lokalizace obličejů ve video sekvencích v reálném čase." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217750.

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My diploma thesis deals about face detection in picture. I try to outline problems of computer vision, artificial intelligence and machine learning. I described in details the proposed detection by Viola and Jones, which uses AdaBoost learning algorithm. This method was deliberately chosen for speed and detection accuracy. This detector was made in programming language C / C + + using the OpenCV library. To a final learning was used database of faces images „MIT CVCL Face Database“. The main goal was to propose the face detector utilizable also in video-sequences.
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Books on the topic "Video - Face Detection"

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Ahuja, Narendra, and Ming-Hsuan Yang. Face Detection and Gesture Recognition for Human-Computer Interaction (The International Series in Video Computing). Springer, 2001.

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Book chapters on the topic "Video - Face Detection"

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Wang, Yuxin, Linsen Song, Wayne Wu, Chen Qian, Ran He, and Chen Change Loy. "Talking Faces: Audio-to-Video Face Generation." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_8.

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AbstractTalking face generation aims at synthesizing coherent and realistic face sequences given an input speech. The task enjoys a wide spectrum of downstream applications, such as teleconferencing, movie dubbing, and virtual assistant. The emergence of deep learning and cross-modality research has led to many interesting works that address talking face generation. Despite great research efforts in talking face generation, the problem remains challenging due to the need for fine-grained control of face components and the generalization to arbitrary sentences. In this chapter, we first discuss the definition and underlying challenges of the problem. Then, we present an overview of recent progress in talking face generation. In addition, we introduce some widely used datasets and performance metrics. Finally, we discuss open questions, potential future directions, and ethical considerations in this task.
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Viallet, Jean Emmanuel, and Olivier Bernier. "Face Detection for Video Summaries." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45479-9_37.

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Hernandez-Ortega, Javier, Ruben Tolosana, Julian Fierrez, and Aythami Morales. "DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_12.

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AbstractThis chapter describes a DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. This chapter explores to what extent rPPG is useful for the detection of DeepFake videos. We analyze the recent fake detector named DeepFakesON-Phys that is based on a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. DeepFakesON-Phys has been experimentally evaluated using the latest public databases in the field: Celeb-DF v2 and DFDC. The results achieved for DeepFake detection based on a single frame are over 98% AUC (Area Under the Curve) on both databases, proving the success of fake detectors based on physiological measurement to detect the latest DeepFake videos. In this chapter, we also propose and study heuristical and statistical approaches for performing continuous DeepFake detection by combining scores from consecutive frames with low latency and high accuracy (100% on the Celeb-DF v2 evaluation dataset). We show that combining scores extracted from short-time video sequences can improve the discrimination power of DeepFakesON-Phys.
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Roy, Ritaban, Indu Joshi, Abhijit Das, and Antitza Dantcheva. "3D CNN Architectures and Attention Mechanisms for Deepfake Detection." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_10.

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AbstractManipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of state-of-the-art video CNNs including 3D ResNet, 3D ResNeXt, and I3D in detecting manipulated videos. In addition, and toward a more robust detection, we investigate the effectiveness of attention mechanisms in this context. Such mechanisms are introduced in CNN architectures in order to ensure that robust features are being learnt. We test two attention mechanisms, namely SE-block and Non-local networks. We present related experimental results on videos tampered by four manipulation techniques, as included in the FaceForensics++ dataset. We investigate three scenarios, where the networks are trained to detect (a) all manipulated videos, (b) each manipulation technique individually, as well as (c) the veracity of videos pertaining to manipulation techniques not included in the train set.
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Korshunov, Pavel, and Sébastien Marcel. "The Threat of Deepfakes to Computer and Human Visions." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_5.

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AbstractDeepfake videos, where a person’s face is automatically swapped with a face of someone else, are becoming easier to generate with more realistic results. The concern for the impact of the widespread deepfake videos on the societal trust in video recordings is growing. In this chapter, we demonstrate how dangerous deepfakes are for both human and computer visions by showing how well these videos can fool face recognition algorithms and naïve human subjects. We also show how well the state-of-the-art deepfake detection algorithms can detect deepfakes and whether they can outperform humans.
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Hao, Hanxiang, Emily R. Bartusiak, David Güera, et al. "Deepfake Detection Using Multiple Data Modalities." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_11.

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AbstractFalsified media threatens key areas of our society, ranging from politics to journalism to economics. Simple and inexpensive tools available today enable easy, credible manipulations of multimedia assets. Some even utilize advanced artificial intelligence concepts to manipulate media, resulting in videos known as deepfakes. Social media platforms and their “echo chamber” effect propagate fabricated digital content at scale, sometimes with dire consequences in real-world situations. However, ensuring semantic consistency across falsified media assets of different modalities is still very challenging for current deepfake tools. Therefore, cross-modal analysis (e.g., video-based and audio-based analysis) provides forensic analysts an opportunity to identify inconsistencies with higher accuracy. In this chapter, we introduce several approaches to detect deepfakes. These approaches leverage different data modalities, including video and audio. We show that the presented methods achieve accurate detection for various large-scale datasets.
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Kurnianggoro, Laksono, and Kang-Hyun Jo. "Attention-Guided Model for Robust Face Detection System." In Image and Video Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34879-3_4.

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Tankus, Ariel, Hezy Yeshurun, and Nathan Intrator. "Face detection by direct convexity estimation." In Audio- and Video-based Biometric Person Authentication. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0015978.

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Tolosana, Ruben, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia. "An Introduction to Digital Face Manipulation." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_1.

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AbstractDigital manipulation has become a thriving topic in the last few years, especially after the popularity of the term DeepFakes. This chapter introduces the prominent digital manipulations with special emphasis on the facial content due to their large number of possible applications. Specifically, we cover the principles of six types of digital face manipulations: (i) entire face synthesis, (ii) identity swap, (iii) face morphing, (iv) attribute manipulation, (v) expression swap (a.k.a. face reenactment or talking faces), and (vi) audio- and text-to-video. These six main types of face manipulation are well established by the research community, having received the most attention in the last few years. In addition, we highlight in this chapter publicly available databases and code for the generation of digital fake content.
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Wu, Haiyuan, Kazumasa Suzuki, Toshikazu Wada, and Qian Chen. "Accelerating Face Detection by Using Depth Information." In Advances in Image and Video Technology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92957-4_57.

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Conference papers on the topic "Video - Face Detection"

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Maadeed, Somaya, Noor Almaadeed, and Omar Elharrouss. "Face Recognition and Summarization for Surveillance Video Sequences." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0235.

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Face recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extract
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Mendes, Paulo, and Sérgio Colcher. "Spatio-temporal Localization of Actors in Video/360-Video and its Applications." In Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/webmedia_estendido.2022.224999.

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The popularity of platforms for storing and transmitting video content has created a substantial volume of video data. Given a set of actors present in a video, generating metadata with the temporal determination of the interval in which each actor is present and their spatial 2D localization in each frame in these intervals can facilitate video retrieval and recommendation. In this work, we investigate Video Face Clustering for this spatio-temporal localization of actors in videos. We first describe our method for Video Face Clustering in which we take advantage of face detection, embeddings,
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Fathima, Annis, V. Vaidehi, S. Vasuhi, Mukund Murali, and S. K. Parulkar. "Pose Invariant Face Detection in Video." In the 2nd International Conference. ACM Press, 2015. http://dx.doi.org/10.1145/2708463.2709054.

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Li-Chun Ming, Diao-Yan Hua, An-Sheng Biao, and Li-Yu Shan. "Face detection and location in video sequences." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620896.

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Pham-Ngoc, Phuong-trinh, and Kang-hyun Jo. "Multi-face Detection System in Video Sequence." In 2006 International Forum on Strategic Technology. IEEE, 2006. http://dx.doi.org/10.1109/ifost.2006.312274.

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Zhang, Xingxian, Chuanchang Liu, and Zhiyuan Su. "Face Detection System Based on Video Stream." In 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC). IEEE, 2017. http://dx.doi.org/10.1109/iccsec.2017.8446978.

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Ya-Feng Deng, Guang-Da Su, Jun Zhou, and Bo Fu. "Fast and robust face detection in video." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527745.

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Wei Song, Ming Li, Guo-sheng Yang, Pei Yang, Chuan-lian Ma, and Jing Yu. "A novel video based face detection algorithm." In International Conference on Cyberspace Technology (CCT 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.1298.

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Patel, Jayant, Amruth Shetty, Amar Vishwakarma, and Vishakha Shelke. "Automated Face Detection And Swapping In Video." In Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India. EAI, 2021. http://dx.doi.org/10.4108/eai.16-5-2020.2303961.

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Kulkarni, Vaibhavi, and Kiran Talele. "Video Analytics for Face Detection and Tracking." In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2020. http://dx.doi.org/10.1109/icacccn51052.2020.9362900.

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Reports on the topic "Video - Face Detection"

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmar
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Johra, Hicham, Martin Veit, Mathias Østergaard Poulsen, et al. Training and testing labelled image and video datasets of human faces for different indoor visual comfort and glare visual discomfort situations. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau542153983.

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Abstract:
The aim of this technical report is to provide a description and access to labelled image and video datasets of human faces that have been generated for different indoor visual comfort and glare visual discomfort situations. These datasets have been used to train and test a computer-vision artificial neural network detecting glare discomfort from images of human faces.
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