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Journal articles on the topic 'Video - Face Detection'

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Published: 4 June 2021

Last updated: 7 September 2023

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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, and speeds it up for highdefinition videos. This project also presents a novel solution to the problem of occlusion and detecting faces in videos. This survey provides an overview of the face detection from video literature, which predominantly focuses on visible wavelength face video as input. For the high-quality videos, we will Face-MTCNN and KLT, for low quality videos we will use MTCNN and KLT. Open issues and challenges are pointed out, i.e., highlighting the importance of comparability for algorithm evaluations and the challenge for future work to create Deep Learning (DL) approaches that are interpretable in addition to Track the faces. The suggested methodology is contrasted with conventional facial feature extraction for every frame and with well-known clustering techniques for a collection of videos

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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 produce a fair analysis of various tracking and detection strategies in order to fulfil the goal of video tracking and detection. Closed-circuit television (CCTV) technology had a significant impact on how crimes were investigated and solved. The material used to review crime scenes was CCTV footage. CCTV systems, on the other hand, just offer footage and do not have the ability to analyse it. In this research, we propose a system that can be integrated with the CCTV footage or any other video input like webcam to detect, recognise, and track a person of interest. Our system will follow people as they move through a space and will be able to detect and recognise human faces. It enables video analytics, allowing existing cameras to be combined with a system that will recognise individuals and track their activities over time. It may be used for remote surveillance and can be integrated into video analytics software and CCTV security solutions as a component. It may be used on college campuses, in offices, and in shopping malls, among other places.

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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 learning algorithms that can detect and validate human faces. Access control, monitoring, and security systems are just a few examples of the many uses for the system. Data gathering, face detection, face recognition, and verification are a few of the processes that the project goes through. A personal identification method called face recognition analyzes a person's physical features to determine their identity to detect and extract facial features from the Image. The process for recognizing faces in humans consists of two phases: face detection, which occurs quickly in people unless the face is nearby, and introduction, which identifies faces as belonging to specific people

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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 sequences into a Bidirectional Long Short-Term Memory (Bi-LSTM), to extract the temporal features. The new scheme is then evaluated on a new large-scale dataset; CelebDF-FaceForencics++ (c23), based on a combination of two popular datasets; FaceForencies++ (c23) and Celeb-DF. It achieves an Area Under the Receiver Operating Characteristic Curve (AUROC) 89.35% score, 89.38% accuracy, 83.15% recall, 85.55% precision, and 84.33% F1-measure for pasting data approach. The experimental analysis approves the superiority of the proposed method compared to the state-of-the-art methods.

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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 challenging scenario such as in real time video face detection and processing. There are many real-time applications where human face serves as identity and these application are time bound so time for detection of face from image or video and the further processing is very essential, thus here our goal is to discuss the face detection system overview and to review various human skin colors based approaches and Haar feature based approach for better detection performance. Detected faces tagging and clustering is essential in some cases, so for such further processing time factor plays important role. Some of the recent approaches to improve detection speed such as using Graphical Processing Unit are discussed and providing future directions in this area.

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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 networks to successfully determine fake vs real face sequences.

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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 facial authentication system are very easy to obtain via the internet or cameras. To solve this problem, in this research proposes a non-spoofing face detection model on video using Faster R-CNN. The results obtained in this study are the Faster R-CNN model that can detect non-spoof and spoof face in real time using the Raspberry Pi as a camera with a frame rate of 1 fps.

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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 to the requirement for securityfor economic transactions, authorization, national safety andsecurity, and other important factors. Exam proctoring platformsshould be capable of detecting cheating and malpractices like face is not on the screen, gaze estimation, mobile phone detection,multiple face detection, etc. This project uses face identificationusing HAAR Cascades Algorithm and face recognition using theLocal Binary Pattern Histogram algorithm. This system can beused in the future in corporate offices, schools, and universities.

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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 video. The spatial features are extracted from the face image using a EfficientNet-B5 method. Spatial feature extractions are fed as input in the Local Binary Pattern Histogram to extract temporal features. The proposed YOLO-LBPH is implemented using the large scale deepfake forensics (DF) dataset known as CelebDF-FaceForensics++(c23), which is a combination of FaceForensics++(c23) and Celeb-DF. As a result, the precision score is 86.88% in the CelebDF-FaceForensics++(c23) dataset, 88.9% in the DFFD dataset, 91.35% in the CASIA-WebFace data. Similarly, the recall is 92.45% in the Celeb-DF-Face Forensics ++(c23) dataset, 93.76% in the DFFD dataset, and 94.35% in the CASIA-Web Face dataset.

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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-based method providing a robust face detection. This verification achieves advantages of the powerful discrimination of Local Binary Patterns (LBPs) and the high speed detection capability of embedded Hidden Markov Models (eHMMs). Experiments were performed with different image databases and video sequences such as NRC-IIT facial video database, Caltech database, etc. Our system is effective in detecting not only frontal faces but also profile, rotated, occluded and connected ones for real-time application.

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Lai, Zhimao, Yufei Wang, Renhai Feng, Xianglei Hu, and Haifeng Xu. "Multi-Feature Fusion Based Deepfake Face Forgery Video Detection." Systems 10, no. 2 (2022): 31. http://dx.doi.org/10.3390/systems10020031.

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With the rapid development of deep learning, generating realistic fake face videos is becoming easier. It is common to make fake news, network pornography, extortion and other related illegal events using deep forgery. In order to attenuate the harm of deep forgery face video, researchers proposed many detection methods based on the tampering traces introduced by deep forgery. However, these methods generally have poor cross-database detection performance. Therefore, this paper proposes a multi-feature fusion detection method to improve the generalization ability of the detector. This method combines feature information of face video in the spatial domain, frequency domain, Pattern of Local Gravitational Force (PLGF) and time domain and effectively reduces the average error rate of span detection while ensuring good detection effect in the library.

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Jain, Mrs Deepali, Sakshi Chaturvedi, Hetvi Patel, and Rajeshwari Jaiswal. "Real-time Face and Object Detection with Age and Gender Prediction for Video Surveillance Applications." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 2358–63. http://dx.doi.org/10.22214/ijraset.2023.49987.

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Abstract: The development of advanced computer vision techniques has made it possible to perform real-time face and object detection for video surveillance applications. Video surveillance is an essential tool for monitoring public areas, buildings, and other locations for security and safety purposes. However, analyzing the vast amount of data generated by video surveillance systems can be challenging. Real-time face detection and object detection systems provide an effective solution to this problem, enabling the identification and tracking of people and objects of interest. In recent years, there has been a growing interest in incorporating age prediction and gender prediction into video surveillance systems. The ability to predict the age and gender of individuals can provide valuable insights to enhance the effectiveness of video surveillance applications. For example, age and gender prediction can be used to detect and prevent potential crimes. This research paper presents a study of a real-time face and object detection system with age and gender prediction for video surveillance applications. The proposed system utilizes deep learning and convolutional neural network techniques to achieve high accuracy in face and object detection, as well as age and gender prediction. The experimental results demonstrate the effectiveness of the proposed system in accurately detecting and tracking faces and objects while predicting their age and gender in real time. The proposed system has potential applications in various fields

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Chen, Qi, Li Yang, Dongping Zhang, Ye Shen, and Shuying Huang. "Face Deduplication in Video Surveillance." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 03 (2017): 1856001. http://dx.doi.org/10.1142/s0218001418560013.

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The video surveillance system based on face analysis has played an increasingly important role in the security industry. Compared with identification methods of other physical characteristics, face verification method is easy to be accepted by people. In the video surveillance scene, it is common to capture multiple faces belonging to a same person. We cannot get a good result of face recognition if we use all the images without considering image quality. In order to solve this problem, we propose a face deduplication system which is combined with face detection and face quality evaluation to obtain the highest quality face image of a person. The experimental results in this paper also show that our method can effectively detect the faces and select the high-quality face images, so as to improve the accuracy of face recognition.

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Li, Xin-ni, and Ya-jun Wang. "Improved Adaboost-Camshift Face Tracking System in Complex Background." March 2023 5, no. 1 (2023): 48–64. http://dx.doi.org/10.36548/jucct.2023.1.004.

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With the rapid growth of science and technology, people pay more and more attention to pattern recognition and computer interaction. Therefore, in the last few years, face detection and tracking technology in video sequence has become a hot topic for people to study. Face tracking detection has a wide application prospect in human-computer interaction, intelligent monitoring, video conference and other aspects. In this paper, the problem of face tracking in video sequences is divided into two aspects: face detection and moving object tracking algorithm. In the face detection problem, the face detection based on Adaboost algorithm is described in detail, and the three-frame difference method is added to make the algorithm better and enhance the speed of face detection. In terms of moving object tracking algorithm, Camshift face tracking algorithm based on color histogram is adopted, which is not affected by the shape and size of the target and has good real-time performance. However, under the influence of color interference and occlusion, the algorithm will make tracking errors. Therefore, Kalman filter is introduced. The algorithm can directly delineate the candidate areas of face to be detected, so as to ensure the feasibility of face tracking. The simulation video image face tracking system is verified by Matlab software. The experimental results show that the system can accurately detect and track the faces in the video image sequence, not only in the simple background, but also in the complex background and multiple faces can also be well detected and tracked, and the tracking ensures real-time performance.

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Sinaga, Anita Sindar R. M. "Seleksi Wajah Digital Menggunakan Algoritma Camshift." JISKA (Jurnal Informatika Sunan Kalijaga) 5, no. 1 (2020): 1. http://dx.doi.org/10.14421/jiska.2020.51-01.

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Real time for digital face database selection using camshift algorithm] Education taken 4-5 years affects physical development. This study uses student digital video data. The recording results are used to identify certain characteristics possessed by a student later stored in the digital file database catalog. The stages of the study consisted of identification, recognition and matching of faces. It starts from converting .mp4 videos to .AVI format. The CAMShift algorithm uses basic HSV colors for tracking face position (tracking) and faces recognition. 1-2 seconds video produces 45-200 frames PNG file. The face matching test results were carried out on several video play, the success of detection: 100% selected, 45%-60%, 80-90%, concluded around 50%-100% successful. Face movements will be caught by the centroid bounding box, if the color of the face is dominant in Hue.

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Liao, Yuxuan, Zhenyu Tang, Jiehong Lei, Jiajia Chen, and Zhong Tang. "Video Face Detection Technology and Its Application in Health Information Management System." Scientific Programming 2022 (February 4, 2022): 1–11. http://dx.doi.org/10.1155/2022/3828478.

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Computer face detection, as an early step and prerequisite for applications such as face recognition and face analysis, has attracted people's attention for a long time. With the popularization of computer applications, the improvement of performance, and the gradual maturity of research in the field of image processing and pattern recognition, face-related applications have become more and more a reality, so the research on face detection and positioning is also receiving more and more attention. Face detection and positioning are an important part of face analysis technology. Its goal is to search for the location of facial features (such as eyes, nose, mouth, and ears) in images or image sequences. It can be widely used in the fields of face tracking, face recognition, gesture recognition, facial expression recognition, head image compression and reconstruction, facial animation, etc. Based on the health information management system, this study mainly discusses the application of face recognition technology in video systems. Compared with other biological characteristics, such as fingerprints and eye masks, human faces are easier to obtain. In research and exploration, stable and effective face detection and face recognition algorithms have been proposed, which can achieve good recognition results even in real-time video surveillance. Aiming at the automatic face recognition technology in video surveillance, this study introduces in detail the video face detection technology in the health information management system of video image collection, image preprocessing, face detection, and face recognition. The prototype system of hygiene management is recognized.

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Wu, Nan, Xin Jin, Qian Jiang, et al. "Multisemantic Path Neural Network for Deepfake Detection." Security and Communication Networks 2022 (October 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/4976848.

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With the continuous development of deep learning techniques, it is now easy for anyone to swap faces in videos. Researchers find that the abuse of these techniques threatens cyberspace security; thus, face forgery detection is a popular research topic. However, current detection methods do not fully use the semantic features of deepfake videos. Most previous work has only divided the semantic features, the importance of which may be unequal, by experimental experience. To solve this problem, we propose a new framework, which is the multisemantic pathway network (MSPNN) for fake face detection. This method comprehensively captures forged information from the dimensions of microscopic, mesoscopic, and macroscopic features. These three kinds of semantic information are given learnable weights. The artifacts of deepfake images are more difficult to observe in a compressed video. Therefore, preprocessing is proposed to detect low-quality deepfake videos, including multiscale detail enhancement and channel information screening based on the compression principle. Center loss and cross-entropy loss are combined to further reduce intraclass spacing. Experimental results show that MSPNN is superior to contrast methods, especially low-quality deepfake video detection.

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Srikanth, G., Adurti Swarnalatha, Thalari Abhishek, Ravula Sai Akhil Patel, and Thalari Swamy. "Missing Person Identification using Machine Learning with Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1264–66. http://dx.doi.org/10.22214/ijraset.2022.47564.

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Abstract: With advances in computing and telecommunications technologies, digital images and video are playing key roles in the present information era. This system uses powerful python algorithm through which the detection and recognition of face is very easy and efficient. Human face is an important biometric object in image and video databases of surveillance systems. Detecting and locating human faces and facial features in an image or image sequence are important tasks in dynamic environments, such as videos, where noise conditions, illuminations, locations of subjects and pose can vary significantly from frame to frame. we want to identify the person based on face data base which we have already created in own data. After that we want to start identification of face using face recognition package. Finally, we will do comparison with data base and we will say weather that person is missing person or unknown person.

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Basurah, Muhammad, Windra Swastika, and Oesman Hendra Kelana. "IMPLEMENTATION OF FACE RECOGNITION AND LIVENESS DETECTION SYSTEM USING TENSORFLOW.JS." Jurnal Informatika Polinema 9, no. 4 (2023): 509–16. http://dx.doi.org/10.33795/jip.v9i4.1332.

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Facial recognition is a popular biometric security system used to authenticate individuals based on their unique facial structure. However, this system is vulnerable to spoofing attacks where the attacker can bypass the system using fake representations of the user's face such as photos, statues or videos. Liveness detection is a method used to address this issue by verifying that the user is a real person and not a representation. This journal article focuses on the life sign method of liveness detection, which utilizes facial movements to confirm the user's existence. We implement the latest technology of artificial intelligence from TensorFlow.js using face-api.js and compare it with the GLCM algorithm. However, even with the life sign detection method, there is still a chance of bypassing the system if an attacker uses a video recording. To mitigate this, we propose the addition of an object detection system to detect the hardware used to show video recordings with ml5.js. Our face recognition and expression detection system, using the pre-trained model face-api.js, achieved an accuracy of 85% and 82.5%, respectively, and the object detection system built with ml5.js has high accuracy and is very effective for liveness detection. Our results indicate that face-api.js outperformed GLCM algorithm in detecting spoofing attempts.

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Nasrabadi, A., and J. Haddadnia. "Human Face Detection by Skin Region Segmentation Filter Applied to Video Images." International Journal of Engineering and Technology 3, no. 1 (2011): 36–39. http://dx.doi.org/10.7763/ijet.2011.v3.197.

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Sang, Hai Feng, Chao Xu, Dan Yang Wu, and Jing Huang. "Research on the Real-Time Multiple Face Detection, Tracking and Recognition Based on Video." Applied Mechanics and Materials 373-375 (August 2013): 442–46. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.442.

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The video images of human face tracking and recognition is a hot research field of biometric recognition and artificial intelligence in recent years. This paper presents an automatic face tracking and recognition system, which can track multiple faces real-timely and recognize the identity. Aiming at Adaboost face detection algorithm is easy to false detection, presents a fusion algorithm based on Adaboost face detection algorithm and Active Shape Model. The algorithm is not only detect face real-timely but also remove the non-face areas; A multi thread CamShift tracking algorithm is proposed for many faces interlaced and face number of changes in the scene . Meanwhile, the algorithm also can identify the faces which have been tracked in the video. The experiment results show that the system is capable of improving the accurate rate of faces detection and recognition in complex backgrounds, and furthermore it also can track the real-time faces effectively.

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Abidin, Muhammad Indra, Ingrid Nurtanio, and Andani Achmad. "Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext." ILKOM Jurnal Ilmiah 14, no. 3 (2022): 178–85. http://dx.doi.org/10.33096/ilkom.v14i3.1254.178-185.

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Deep-fake in videos is a video synthesis technique by changing the people’s face in the video with others’ face. Deep-fake technology in videos has been used to manipulate information, therefore it is necessary to detect deep-fakes in videos. This paper aimed to detect deep-fakes in videos using the ResNext Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms. The video data was divided into 4 types, namely video with 10 frames, 20 frames, 40 frames and 60 frames. Furthermore, face detection was used to crop the image to 100 x 100 pixels and then the pictures were processed using ResNext CNN and LSTM. The confusion matrix was employed to measure the performance of the ResNext CNN-LSTM algorithm. The indicators used were accuracy, precision, and recall. The results of data classification showed that the highest accuracy value was 90% for data with 40 and 60 frames. While data with 10 frames had the lowest accuracy with 52% only. ResNext CNN-LSTM was able to detect deep-fakes in videos well even though the size of the image was small.

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Machaca Arceda, V. E., K. M. Fernández Fabián, P. C. Laguna Laura, J. J. Rivera Tito, and J. C. Gutiérrez Cáceres. "Fast Face Detection in Violent Video Scenes." Electronic Notes in Theoretical Computer Science 329 (December 2016): 5–26. http://dx.doi.org/10.1016/j.entcs.2016.12.002.

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Liu, Weiwei. "Video Face Detection Based on Deep Learning." Wireless Personal Communications 102, no. 4 (2018): 2853–68. http://dx.doi.org/10.1007/s11277-018-5311-7.

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Lin, Yih-Kai, and Hao-Lun Sun. "Few-Shot Training GAN for Face Forgery Classification and Segmentation Based on the Fine-Tune Approach." Electronics 12, no. 6 (2023): 1417. http://dx.doi.org/10.3390/electronics12061417.

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There are many techniques for faking videos that can alter the face in a video to look like another person. This type of fake video has caused a number of information security crises. Many deep learning-based detection methods have been developed for these forgery methods. These detection methods require a large amount of training data and thus cannot develop detectors quickly when new forgery methods emerge. In addition, traditional forgery detection refers to a classifier that outputs real or fake versions of the input images. If the detector can output a prediction of the fake area, i.e., a segmentation version of forgery detection, it will be a great help for forensic work. Thus, in this paper, we propose a GAN-based deep learning approach that allows detection of forged regions using a smaller number of training samples. The generator part of the proposed architecture is used to synthesize predicted segmentation which indicates the fakeness of each pixel. To solve the classification problem, a threshold on the percentage of fake pixels is used to decide whether the input image is fake. For detecting fake videos, frames of the video are extracted and it is detected whether they are fake. If the percentage of fake frames is higher than a given threshold, the video is classified as fake. Compared with other papers, the experimental results show that our method has better classification and segmentation.

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Deshmukh, Amar B., and N. Usha Rani. "Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution." International Journal of Digital Crime and Forensics 12, no. 3 (2020): 77–95. http://dx.doi.org/10.4018/ijdcf.2020070106.

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One of the major challenges faced by video surveillance is recognition from low-resolution videos or person identification. Image enhancement methods play a significant role in enhancing the resolution of the video. This article introduces a technique for face super resolution based on a deep convolutional neural network (Deep CNN). At first, the video frames are extracted from the input video and the face detection is performed using the Viola-Jones algorithm. The detected face image and the scaling factors are fed into the Fractional-Grey Wolf Optimizer (FGWO)-based kernel weighted regression model and the proposed Deep CNN separately. Finally, the results obtained from both the techniques are integrated using a fuzzy logic system, offering a face image with enhanced resolution. Experimentation is carried out using the UCSD face video dataset, and the effectiveness of the proposed Deep CNN is checked depending on the block size and the upscaling factor values and is evaluated to be the best when compared to other existing techniques with an improved SDME value of 80.888.

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Nurhopipah, Ade, and Agus Harjoko. "Motion Detection and Face Recognition for CCTV Surveillance System." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 12, no. 2 (2018): 107. http://dx.doi.org/10.22146/ijccs.18198.

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Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system. This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used for motion detection, and Haar Classifiers Cascade used for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN). Offline tests performed on 45 CCTV video. The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a face detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time.

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Gaurav Melkani and Dr. Sunil Maggu. "Image-Based Face Detection and Recognition." International Journal for Modern Trends in Science and Technology 6, no. 12 (2021): 466–70. http://dx.doi.org/10.46501/ijmtst061290.

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Face recognition from image or video is a popular topic in biometrics research. Many public places usually have surveillance cameras for video capture and these cameras have their significant value for security purpose. It is widely acknowledgedthatthefacerecognitionhaveplayedanimportant role in surveillance system as it doesn’t need the object’s cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. As human face is a dynamic object having high degree of variability in its appearance, that makes face detection a difficult problem in computer vision. In this field, accuracy and speed of identification is a mainissue. The goal of this paper is to evaluate various face detection and recognition methods, provide complete solution for image based face detection and recognition with higher accuracy, better response rate as an initial step for video surveillance. Solution is proposed based on performed tests on various face rich databases in terms of subjects, pose, emotions, race andlight.

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Kassab, Kenan, Alexey Kashevnik, Alexander Mayatin, and Dmitry Zubok. "VPTD: Human Face Video Dataset for Personality Traits Detection." Data 8, no. 7 (2023): 113. http://dx.doi.org/10.3390/data8070113.

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In this paper, we propose a dataset for personality traits detection based on human face videos. Ground truth data have been annotated using the IPIP-50 personality test that every participant is implementing. To collect the dataset, we developed a web-based platform that allows us to acquire spontaneous answers for predefined questions from the respondents. The website allows the participants to record an interactive interview in order to imitate the real-life interview. The dataset includes 38 videos (2 min on average) for people of different races, genders, and ages. In the paper, we propose the top five personality traits calculated based on the test, as well as the top five personality traits calculated by our own developed model that determines this information based on video analysis. We introduced a statistical analysis for the collected dataset, and we also applied a K-means clustering algorithm to cluster the data and present the clustering results.

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Fatah, Yunan Kholilul, Yosi Kristian, and Devi Dwi Purwanto. "Sistem Drone Cerdas Yang Dilengkapi Face Detection dan Face Recognition Untuk Pembuatan Sinematik Video." Journal of Information System,Graphics, Hospitality and Technology 4, no. 01 (2022): 32–38. http://dx.doi.org/10.37823/insight.v4i01.192.

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Pembuatan konten video melalui drone pada umumnya membutuhkan seorang professional untuk mengendalikan drone tersebut agar mampu menghasilkan pergerakan drone dengan hasil video yang diinginkan. Video sinematik adalah video yang mempunyai alur cerita atau dapat menyampaikan sebuah cerita pada film ataupun video pendek. Pengarahan alur cerita tersebut membutuhkan juga seorang film director yang mengarahkan adegan yang akan ditampilkan didalam video. Penelitian ini mengusulkan untuk membuat autonous drone agar mampu bergerak dan menangkap video sesuai dengan arahan film director. Penelitian ini menggunakan face detection dan face recognition dengan algorithma Local Binary Pattern Histogram (LBPH) dan memanfaatkan Rule base system sebagai system cerdas yang terdapat pada system agar drone mampu mengikuti wajah yang dikenali sesuai pergerakan subject yang telah di rencanakan oleh film director. Setiap pergerakan drone memiliki catatan terbang yang terdapat pada system drone berupa Inertial Measurement Unit (IMU) sehingga system mampu memberikan grafik 3 dimensi setelah drone sudah tidak berada di udara. Skenario yang diusulkan dalam penelitian ini membuktikan bahwa drone mampu bergerak sesuai ekspektasi penulis dan film director. Selain itu survey berupa kuesioner untuk responden umum juga membuktikan bahwa drone sudah mengikuti salah satu konsep cinematography seperti jarak sudut pandang, object yang menarik (shape saliency) dan area pergerakan kamera.

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Chang, Chuang Jan, and Shu Lin Hwang. "LSO-AdaBoost Based Face Detection for IP-CAM Video." Applied Mechanics and Materials 284-287 (January 2013): 3543–48. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3543.

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The IP-CAM plays a major role in the context of digital video surveillance systems. The function of face detection can add extra value and can contribute towards an intelligent video surveillance system. The cascaded AdaBoost-based face detection system proposed by Viola can support real-time detection with a high detection rate. The performance of the Alt2 cascade (from OpenCV) in an IP-CAM video is worse than that with regard to static images because the training data set in the Alt2 cannot consider the localized characters in the special IP-CAM video. Therefore, this study presents an enhanced training method using the Adaboost algorithm which is capable of obtaining the localized sampling optimum (LSO) from a local IP-CAM video. In addition, we use an improved motion detection algorithm that cooperates with the former face detector to speed up processing time and achieve a better detection rate on video-rate processing speed. The proposed solution has been developed around the cascaded AdaBoost approach, using the open-CV library, with a LSO from a local IP-CAM video. An efficient motion detection model is adopted for practical applications. The overall system performance using 30% local samples can be improved to a 97.9% detection rate and reduce detection time by 54.5% with regard to the Alt2 cascade.

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Alshifa, S. "Face Mask and Social Distancing Detection Using ML Technique." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3218–22. http://dx.doi.org/10.22214/ijraset.2021.37021.

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Detecting Mask and Social Distance is our main motive in this project.Face detection plays important roles in detecting face mask. Face detection means detecting or searching for a face in an image or video. For face and mask detection we use viola jones algorithm or Haar cascade algorithm using Open CV. For social distancing we use YOLO algorithm. We have created a system which detect the face and then, it will detect nose and mouth to confirm that the person wear mask or not.

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Deng, Liwei, Hongfei Suo, and Dongjie Li. "Deepfake Video Detection Based on EfficientNet-V2 Network." Computational Intelligence and Neuroscience 2022 (April 15, 2022): 1–13. http://dx.doi.org/10.1155/2022/3441549.

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As technology advances and society evolves, deep learning is becoming easier to operate. Many unscrupulous people are using deep learning technology to create fake pictures and fake videos that seriously endanger the stability of the country and society. Examples include faking politicians to make inappropriate statements, using face-swapping technology to spread false information, and creating fake videos to obtain money. In view of this social problem, based on the original fake face detection system, this paper proposes using a new network of EfficientNet-V2 to distinguish the authenticity of pictures and videos. Moreover, our method was used to deal with two current mainstream large-scale fake face datasets, and EfficientNet-V2 highlighted the superior performance of the new network by comparing the existing detection network with the actual training and testing results. Finally, based on improving the accuracy of the detection system in distinguishing real and fake faces, the actual pictures and videos are detected, and an excellent visualization effect is achieved.

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Amjed, Noor, Fatimah Khalid, Rahmita Wirza O. K. Rahmat, and Hizmawati Bint Madzin. "A Robust Geometric Skin Colour Face Detection Method under Unconstrained Environment of Smartphone Database." Applied Mechanics and Materials 892 (June 2019): 31–37. http://dx.doi.org/10.4028/www.scientific.net/amm.892.31.

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Face detection is the primary task in building a vision-based human-computer interaction system and in special applications such as face recognition, face tracking, face identification, expression recognition and also content-based image retrieval. A potent face detection system must be able to detect faces irrespective of illuminations, shadows, cluttered backgrounds, orientation and facial expressions. In previous literature, many approaches for face detection had been proposed. However, face detection in outdoor images with uncontrolled illumination and images with complex background are still a serious problem. Hence, in this paper, we had proposed a Geometric Skin Colour (GSC) method for detecting faces accurately in real world image, under capturing conditions of both indoor and outdoor, and with a variety of illuminations and also in cluttered backgrounds. The selected method was evaluated on two different face video smartphone databases and the obtained results proved the outperformance of the proposed method under the unconstrained environment of these databases.

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Ismail, Aya, Marwa Elpeltagy, Mervat S. Zaki, and Kamal Eldahshan. "A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost." Sensors 21, no. 16 (2021): 5413. http://dx.doi.org/10.3390/s21165413.

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Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. Due to their devastating impacts on the world, distinguishing between real and deepfake videos has become a fundamental issue. This paper presents a new deepfake detection method: you only look once–convolutional neural network–extreme gradient boosting (YOLO-CNN-XGBoost). The YOLO face detector is employed to extract the face area from video frames, while the InceptionResNetV2 CNN is utilized to extract features from these faces. These features are fed into the XGBoost that works as a recognizer on the top level of the CNN network. The proposed method achieves 90.62% of an area under the receiver operating characteristic curve (AUC), 90.73% accuracy, 93.53% specificity, 85.39% sensitivity, 85.39% recall, 87.36% precision, and 86.36% F1-measure on the CelebDF-FaceForencics++ (c23) merged dataset. The experimental study confirms the superiority of the presented method as compared to the state-of-the-art methods.

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Khan, Amjad Rehman, Majid Harouni, Sepideh Sharifi, Saeed Ali Bahaj, and Tanzila Saba. "Face Detection in Close-up Shot Video Events Using Video Mining." Journal of Advances in Information Technology 14, no. 2 (2023): 160–67. http://dx.doi.org/10.12720/jait.14.2.160-167.

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Zhang, Huijun, Ling Feng, Ningyun Li, Zhanyu Jin, and Lei Cao. "Video-Based Stress Detection through Deep Learning." Sensors 20, no. 19 (2020): 5552. http://dx.doi.org/10.3390/s20195552.

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Stress has become an increasingly serious problem in the current society, threatening mankind’s well-beings. With the ubiquitous deployment of video cameras in surroundings, detecting stress based on the contact-free camera sensors becomes a cost-effective and mass-reaching way without interference of artificial traits and factors. In this study, we leverage users’ facial expressions and action motions in the video and present a two-leveled stress detection network (TSDNet). TSDNet firstly learns face- and action-level representations separately, and then fuses the results through a stream weighted integrator with local and global attention for stress identification. To evaluate the performance of TSDNet, we constructed a video dataset containing 2092 labeled video clips, and the experimental results on the built dataset show that: (1) TSDNet outperformed the hand-crafted feature engineering approaches with detection accuracy 85.42% and F1-Score 85.28%, demonstrating the feasibility and effectiveness of using deep learning to analyze one’s face and action motions; and (2) considering both facial expressions and action motions could improve detection accuracy and F1-Score of that considering only face or action method by over 7%.

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Ayo, Femi Emmanuel, Abiodun Muyideen Mustapha, Joachim Ayodeji Braimah, and Daniel Ayodele Aina. "Geometric Analysis and YOLO Algorithm for Automatic Face Detection System in a Security Setting." Journal of Physics: Conference Series 2199, no. 1 (2022): 012010. http://dx.doi.org/10.1088/1742-6596/2199/1/012010.

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Abstract Face detection is a computer technology that determines the location and size of the human face in an arbitrary digital image employed for authentication. Face recognition technology plays a vital role in network security, video compression, content indexing, and retrieval since "humans" are the focal point of many videos. Face recognition-based network access control makes it almost difficult for hackers to obtain a user’s "password" and improves user-friendliness in human-computer interaction. In this paper, an automatic face detection system that accurately detects human faces and ignores any other object that is not a human face using geometric analysis and the you-only-look-once (YOLO) algorithm is introduced. The system is able to predict the ages and genders of the faces detected. It also detects the facial landmarks of the faces and indicates the emotions of the faces detected. A sample of four faces is considered for testing the system; thus, accurately detecting gender and emotion but not age correctly. In all, the evaluation shows about 80% accuracy. With the results got, the system can support security and analytics. It can be used to get analytics in an event with a lot of attendees and can also be used to get a facial mapping of someone involved in a crime scene for security purposes.

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Patil, Santosh, N. Ramakrishnaiah, and S. Laxman Kumar. "Enhanced approach for face detection and identifying human body proportionality using v-jones algorithm." International Journal of Engineering & Technology 7, no. 4 (2018): 2374. http://dx.doi.org/10.14419/ijet.v7i4.14734.

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Manual analysis of pedestrians and crowds is often impractical for massive datasets of surveillance videos. Automatic tracking of humans is one of the essential abilities for computerized analysis of such videos. In this proposed work we use Viola jones method for detecting moving human object, next using same method we identify the Human anatomy body proportion to detect the whole human body. The final function is the skin color threshold using the HIS and YCbCr. The proposed method yields high accuracy, we conducted experimental analysis on different videos, achieved high accuracy in detecting human object moment. Several future enhancements can be made to the system. The detection and tracking of multiple people can be extended to real-time live video. Apart from the detection and tracking, process of recognition can also be done.

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Hasso, Maha, and Shahad Hasso. "Face Detection in a Video File Based on Matching Face Template." AL-Rafidain Journal of Computer Sciences and Mathematics 10, no. 2 (2013): 159–72. http://dx.doi.org/10.33899/csmj.2013.163492.

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Mliki, Hazar, and Mohamed Hammami. "Face analysis in video: face detection and tracking with pose estimation." International Journal of Biometrics 10, no. 2 (2018): 121. http://dx.doi.org/10.1504/ijbm.2018.091625.

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Awotunde, Joseph Bamidele, Rasheed Gbenga Jimoh, Agbotiname Lucky Imoize, Akeem Tayo Abdulrazaq, Chun-Ta Li, and Cheng-Chi Lee. "An Enhanced Deep Learning-Based DeepFake Video Detection and Classification System." Electronics 12, no. 1 (2022): 87. http://dx.doi.org/10.3390/electronics12010087.

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The privacy of individuals and entire countries is currently threatened by the widespread use of face-swapping DeepFake models, which result in a sizable number of fake videos that seem extraordinarily genuine. Because DeepFake production tools have advanced so much and since so many researchers and businesses are interested in testing their limits, fake media is spreading like wildfire over the internet. Therefore, this study proposes five-layered convolutional neural networks (CNNs) for a DeepFake detection and classification model. The CNN enhanced with ReLU is used to extract features from these faces once the model has extracted the face region from video frames. To guarantee model accuracy while maintaining a suitable weight, a CNN enabled with ReLU model was used for the DeepFake-detection-influenced video. The performance evaluation of the proposed model was tested using Face2Face, and first-order motion DeepFake datasets. Experimental results revealed that the proposed model has an average prediction rate of 98% for DeepFake videos and 95% for Face2Face videos under actual network diffusion circumstances. When compared with systems such as Meso4, MesoInception4, Xception, EfficientNet-B0, and VGG16 which utilizes the convolutional neural network, the suggested model produced the best results with an accuracy rate of 86%.

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Peng, Xiao Ming. "Recent Progress in Human Face Detection, Tracking and Recognition." Advanced Materials Research 760-762 (September 2013): 1539–46. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1539.

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Facial recognition is an important research topic in biometrics and has wide applications in pattern recognition and computer vision. This paper aims at providing interested readers with recent progresses in human face detection, tracking, video-based face recognition, and 3D+2D hybrid face recognition. For this purpose, it mainly focuses on those state-of-the-arts methods and technologies that emerged in recent previous few years. Most existing methods in this area are still-image based which do not utilize motion information; whereas most video-based methods work only in 2D video sequences, which are subject to pose and illumination variations. The recent emergence of 3D video cameras capable of producing range image sequences and corresponding texture image sequences simultaneously allows for the possibility of facial recognition in a 3D+2D video-based scenario. In view of this fact, a scheme of face detection, tracking and recognition process in 3D video-based manner is also proposed in this paper with further concerns addressed.

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Wu, Xiao Kang, Cheng Gang Xie, and Qin Lu. "Algorithm of Video Decomposition and Video Abstraction Generation Based on Face Detection and Recognition." Applied Mechanics and Materials 644-650 (September 2014): 4620–23. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4620.

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ion generation based on face detection and recognitionXiaokang Wu1, a, Chenggang Xie2, b, Qin Lu2, c1 National University Of Defense Technology, Changsha 410073, China;a172896292@qq.com, bqingqingzijin_k@126.com,Keywords: face detection, face recognition, key frame, video abstractionAbstract. In order to facilitate users browse the behaviors and expressions of interesting objects in a video quickly, need to remove the redundancy information and extract key frames related to the object interested. This paper uses a fast face detection based on skin color, and recognition technology using spectrum feature matching, decompose the coupling video, and classify frames related to the object into different sets, generate a different video abstraction of each object. Experimental results show that the algorithm under different light conditions has better practicability.

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Razzaq, Ali Nadhim, Rozaida Ghazali, Nidhal Khdhair El Abbadi, and Mohammad Dosh. "A Comprehensive Survey on Face Detection Techniques." Webology 19, no. 1 (2022): 613–28. http://dx.doi.org/10.14704/web/v19i1/web19044.

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The need for automatic understanding and examination of data increased with the tremendous growth of video and imaging databases. The change of identity, feelings and attitudes of a person's face always play a key role in terms of social communication. It is difficult for human beings to distinguish and identify various faces. Hence, we can say that in face recognition, the automatic computer-aided face detection system plays an important role. It also plays a significant role in determining the facial expressions and their recognition, estimation of head pose and interaction of humans and computers, etc. The size and location of the human face in a digital image are determined by face detection. For face detection in digital images, this paper brings forward a detailed and comprehensive survey of various important techniques. In this paper, different challenges and applications regarding face detection are also discussed. The standard databases for the detection of the face are mentioned along with various other features. Along with this, special discussions are provided regarding highly practical aspects for the robustness of the system for face detection. In the end, there ¬are some highly promising directions for the research and investigation to be conducted in the future.

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Pople, Vedant, Ayush Kanaujia, R. Vijayan, and V. Mareeswari. "Face Mask Detection for Real Time Video Streams." ECS Transactions 107, no. 1 (2022): 8275–87. http://dx.doi.org/10.1149/10701.8275ecst.

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The Face Mask Detection model is used to make sure a person is wearing a mask or not. This model results from the grappling situation presented by COVID-19, resulting in the mandatory use of masks at public places. Security agencies need to plant actual personnel to make sure all the people in public are wearing ‘masks’, this model will lessen the risk of people being contacted by COVID-19. This research helps us understand a broader perspective about the Face Mask Detection models by comparing different state of the art models. The model uses MobileNetV2 architecture that has inverted bottlenecks and depth-wise convolutions to filter features. The complete model is built in two phases, the first one consisting of making a Face Mask Detection model trained to detect the face and mask, and then placing it in the Real Time environment by using the OpenCV for actually predicting the usage of face mask.

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Korshunov, Pavel, and Wei Tsang Ooi. "Video quality for face detection, recognition, and tracking." ACM Transactions on Multimedia Computing, Communications, and Applications 7, no. 3 (2011): 1–21. http://dx.doi.org/10.1145/2000486.2000488.

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Garg, Dweepna, Priyanka Jain, Ketan Kotecha, Parth Goel, and Vijayakumar Varadarajan. "An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence." Big Data and Cognitive Computing 6, no. 1 (2022): 9. http://dx.doi.org/10.3390/bdcc6010009.

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In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).

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Amirgaliyev, Ye, A. Sadykova, and Ch Kenshimov. "COMPARISION OF FACE DETECTION TOOLS." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (2021): 59–64. http://dx.doi.org/10.51889/2021-4.1728-7901.08.

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This article examines the assessment of tools such as Dlib, OpenCV, MTCNN, FaceNet for face recognition. In the process of work, the execution time and the count of detecting of each tool were determined and calculated. The results pictured in graph choose the right tool according to the data obtained in the article that was optimal for next research works. The choice was made for the ease of writing a parallel algorithm. The rationale for the choice of the tool is also given according to the parameters of the use of machine resources, which makes it possible to optimally select a machine without additional and large costs. A comparative analysis of each instrument was performed and the results were identified accordingly. Based on the test results, we divided two cases and tried to give recommendations for each of them. The first case is triggered if only quick face detection is considered in the video. The second case is triggered if more faces are viewed in the video. It turned out that in the first case, we need to use the Dlib tool. In the second case, we can choose tools like Facenet or Mtcnn. The results obtained in the process of the research are presented in the form of graphs, tables and recorded in the conclusion section of this article.

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