Academic literature on the topic 'Multiple face detection'

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Journal articles on the topic "Multiple face detection"

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S. Hasan, Athraa, Jianjun Yi, Haider M. AlSabbagh, and Liwei Chen. "Multiple Object Detection-Based Machine Learning Techniques." Iraqi Journal for Electrical and Electronic Engineering 20, no. 1 (2024): 149–59. http://dx.doi.org/10.37917/ijeee.20.1.15.

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Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%
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Xiong, Yuyang, Wei Meng, Junwei Yan, and Jun Yang. "A Rotation-Invariance Face Detector Based on RetinaNet." Journal of Physics: Conference Series 2562, no. 1 (2023): 012066. http://dx.doi.org/10.1088/1742-6596/2562/1/012066.

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Abstract The use of deep convolutional neural networks has greatly improved the performance of general face detection. For detecting rotated faces, the mainstream approach is to use multi-stage detectors to gradually adjust the rotated face to a vertical orientation for detection, which increases the complexity of training as multiple networks are involved. In this study, we propose a new method for rotation-invariant face detection, which abandons the previously used cascaded architecture with multiple stages and instead uses a single-stage detector to achieve end-to-end detection of face classification, face box regression, and facial landmark regression. Extensive experiments on FDDB in multiple orientations have shown the effectiveness of our method. The results demonstrate that our method achieves good detection performance and the detection accuracy of our method even exceeds that of other rotated face detectors on the front-facing FDDB dataset.
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Chandrashekar, T. R., K. B. ShivaKumar, A. Srinidhi G, and A. K. Goutam. "PCA Based Rapid and Real Time Face Recognition Technique." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 12 (2013): 385–90. https://doi.org/10.5281/zenodo.14613535.

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Economical and efficient that is used in various applications is face Biometric which has been a popular form biometric system. Face recognition system is being a topic of research for last few decades. Several techniques are proposed to improve the performance of face recognition system. Accuracy is tested against intensity, distance from camera, and pose variance. Multiple face recognition is another subtopic which is under research now a day. Speed at which the technique works is a parameter under consideration to evaluate a technique. As an example a support vector machine performs really well for face recognition but the computational efficiency degrades significantly with increase in number of classes. Eigen Face technique produces quality features for face recognition but the accuracy is proved to be comparatively less to many other techniques. With increase in use of core processors in personal computers and application demanding speed in processing and multiple face detection and recognition system (for example an entry detection system in shopping mall or an industry), demand for such systems are cumulative as there is a need for automated systems worldwide. In this paper we propose a novel system of face recognition developed with C# .Net that can detect multiple faces and can recognize the faces parallel by utilizing the system resources and the core processors. The system is built around Haar Cascade based face detection and PCA based face recognition system with C#.Net. Parallel library designed for .Net is used to aide to high speed detection and recognition of the real time faces. Analysis of the performance of the proposed technique with some of the conventional techniques reveals that the proposed technique is not only accurate, but also is fast in comparison to other techniques. 
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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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Mohammed, W. Al-Neama, A. Mohamad Alshiha Abeer, and Ghanem Saeed Mustafa. "A parallel algorithm of multiple face detection on multi-core system." A parallel algorithm of multiple face detection on multi-core system 29, no. 2 (2023): 1166–73. https://doi.org/10.11591/ijeecs.v29.i2.pp1166-1173.

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This work offers a graphics processing unit (GPU)-based system for real-time face recognition, which can detect and identify faces with high accuracy. This work created and implemented novel parallel strategies for image integral, computation scan window processing, and classifier amplification and correction as part of the face identification phase of the Viola-Jones cascade classifier. Also, the algorithm and parallelized a portion of the testing step during the facial recognition stage were experimented with. The suggested approach significantly improves existing facial recognition methods by enhancing the performance of two crucial components. The experimental findings show that the proposed method, when implemented on an NVidia GTX 570 graphics card, outperforms the typical CPU program by a factor of 19.72 in the detection phase and 1573 in the recognition phase, with only 2000 images trained and 40 images tested. The recognition rate will plateau when the hardware's capabilities are maxed out. This demonstrates that the suggested method works well in real-time.
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Sun, Ke, Hong Liu, Qixiang Ye, et al. "Domain General Face Forgery Detection by Learning to Weight." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 2638–46. http://dx.doi.org/10.1609/aaai.v35i3.16367.

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In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that guarantees face detection performance across multiple domains, particularly the target domains that are never seen before. However, various face forgery methods cause complex and biased data distributions, making it challenging to detect fake faces in unseen domains. We argue that different faces contribute differently to a detection model trained on multiple domains, making the model likely to fit domain-specific biases. As such, we propose the LTW approach based on the meta-weight learning algorithm, which configures different weights for face images from different domains. The LTW network can balance the model's generalizability across multiple domains. Then, the meta-optimization calibrates the source domain's gradient enabling more discriminative features to be learned. The detection ability of the network is further improved by introducing an intra-class compact loss. Extensive experiments on several commonly used deepfake datasets to demonstrate the effectiveness of our method in detecting synthetic faces. Code and supplemental material are available at https://github.com/skJack/LTW.
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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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Ms., Manisha Loharkar, Prof. Shital Wagh Ass, and Swati Bhavsar Dr. "MULTIPLE FACE DETECTION SYSTEM USING DEEP LEARNING." Journal of the Maharaja Sayajirao University of Baroda 59, no. 1 (I) (2025): 352–66. https://doi.org/10.5281/zenodo.15237844.

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AbstractThe smart classroom leverages automation to streamline tasks such as attendance registration, whichtraditionally require considerable time and effort. Conventional methods—including identificationcards, radio frequency systems, and biometric technologies—often face limitations related to safety,accuracy, and cost. However, recent advancements in digital image processing, particularly facerecognition technology, present a promising alternative.This study introduces an automated attendancesystem utilizing the YOLOv8 algorithm, capable of detecting and recognizing multiple student facessimultaneously with high efficiency. The system was tested on a real time dataset and achieved up to90-95% accuracy, highlighting its reliability and effectiveness in automating attendance processes.The proposed system not only automates the attendance marking process but also generates analyticalreports. Face recognition plays a vital role in uniquely identifying individuals, making it an idealsolution for classroom attendance automation through the integration of advanced face detectiontechniques. Keywords: Attendance, Face Recognition, HOG, LBPH, MySQL, YOLOv8, CNN
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Pailus, Rayner, and Rayner Alfred. "Performance Evaluation of MadBoost on Face Detection." Applied Mechanics and Materials 892 (June 2019): 200–209. http://dx.doi.org/10.4028/www.scientific.net/amm.892.200.

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Adaboost Viola-Jones method is indeed a profound discovery in detecting face images mainly because it is fast, light and one of the easiest methods of detecting face images among other techniques of face detection. Viola Jones uses Haar wavelet filter to detect face images and it produces almost 80%accuracy of face detection. This paper discusses proposed methodology and algorithms that involved larger library of filters used to create more discrimination features among the images by processing the proposed 15 Haar rectangular features (an extension from 4 Haar wavelet filters of Viola Jones) and used them in multiple adaptive ensemble process of detecting face image. After facial detection, the process continues with normalization processes by applying feature extraction such as PCA combined with LDA or LPP to extract our week learners’ wavelet for more classification features. Upon the process of feature extraction proposed feature selection to index these extracted data. These extracted vectors are used for training and creating MADBoost (Multiple Adaptive Diversified Boost)(an improvement of Adaboost, which uses multiple feature extraction methods combined with multiple classifiers) is able to capture, recognize and distinguish face image (s) faster. MADBoost applies the ensemble approach with better weights for classification to produce better face recognition results. Three experiments have been conducted to investigate the performance of the proposed MADBoost with three other classifiers, Neural Network (NN), Support Vector Machines (SVM) and Adaboost classifiers using Principal Component Analysis (PCA) as the feature extraction method. These experiments were tested against obstacles of POIES (Pose, Obstruction, Illumination, Expression, Sizes). Based on the results obtained, Madboost is found to be able to improve the recognition performance in matching failures, incorrect matching, matching success percentages and acceptable time taken to perform the classification task.
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Cheng, Xiao Ge, Amir As'ari Muhammad, and Anis Jasmin Sufri Nur. "Multiple face mask wearer detection based on YOLOv3 approach." International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 384–93. https://doi.org/10.11591/ijai.v12.i1.pp384-393.

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The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images.
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Dissertations / Theses on the topic "Multiple face detection"

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Bouganis, Christos-Savvas. "Multiple light source detection with application to face recognition." Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/11322.

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Higgs, David Robert. "Parts-based object detection using multiple views /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1000.

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Minary, Pauline. "Evidential calibration and fusion of multiple classifiers : application to face blurring." Thesis, Artois, 2017. http://www.theses.fr/2017ARTO0207/document.

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Afin d’améliorer les performances d’un problème de classification, une piste de recherche consiste à utiliser plusieurs classifieurs et à fusionner leurs sorties. Pour ce faire, certaines approches utilisent une règle de fusion. Cela nécessite que les sorties soient d’abord rendues comparables, ce qui est généralement effectué en utilisant une calibration probabiliste de chaque classifieur. La fusion peut également être réalisée en concaténant les sorties et en appliquant à ce vecteur une calibration probabiliste conjointe. Récemment, des extensions des calibrations d’un classifieur individuel ont été proposées en utilisant la théorie de l’évidence, afin de mieux représenter les incertitudes. Premièrement, cette idée est adaptée aux techniques de calibrations probabilistes conjointes, conduisant à des versions évidentielles. Cette approche est comparée à celles mentionnées ci-dessus sur des jeux de données de classification classiques. Dans la seconde partie, le problème d’anonymisation de visages sur des images, auquel SNCF doit répondre, est considéré. Une méthode consiste à utiliser plusieurs détecteurs de visages, qui retournent des boites et des scores de confiance associés, et à combiner ces sorties avec une étape d’association et de calibration évidentielle. Il est montré que le raisonnement au niveau pixel est plus intéressant que celui au niveau boite et que, parmi les approches de fusion abordées dans la première partie, la calibration conjointe évidentielle donne les meilleurs résultats. Enfin, le cas des images provenant de vidéos est considéré. Pour tirer parti de l’information contenue dans les vidéos, un algorithme de suivi classique est ajouté au système<br>In order to improve overall performance of a classification problem, a path of research consists in using several classifiers and to fuse their outputs. To perform this fusion, some approaches merge the outputs using a fusion rule. This requires that the outputs be made comparable beforehand, which is usually done using a probabilistic calibration of each classifier. The fusion can also be performed by concatenating the classifier outputs into a vector, and applying a joint probabilistic calibration to it. Recently, extensions of probabilistic calibrations of an individual classifier have been proposed using evidence theory, in order to better represent the uncertainties inherent to the calibration process. In the first part of this thesis, this latter idea is adapted to joint probabilistic calibration techniques, leading to evidential versions. This approach is then compared to the aforementioned ones on classical classification datasets. In the second part, the challenging problem of blurring faces on images, which SNCF needs to address, is tackled. A state-of-the-art method for this problem is to use several face detectors, which return boxes with associated confidence scores, and to combine their outputs using an association step and an evidential calibration. In this report, it is shown that reasoning at the pixel level is more interesting than reasoning at the box-level, and that among the fusion approaches discussed in the first part, the evidential joint calibration yields the best results. Finally, the case of images coming from videos is considered. To leverage the information contained in videos, a classical tracking algorithm is added to the blurring system
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Lee, Yeongseon. "Bayesian 3D multiple people tracking using multiple indoor cameras and microphones." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29668.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Rusell M. Mersereau; Committee Member: Biing Hwang (Fred) Juang; Committee Member: Christopher E. Heil; Committee Member: Georgia Vachtsevanos; Committee Member: James H. McClellan. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Bai, Yung-Ting, and 白詠霆. "Face Detection Based on Multiple Criteria." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/68230621622235806980.

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碩士<br>國立交通大學<br>資訊學院資訊科技產業專班<br>98<br>Face detection is an important research topic in recent years. Automatic face detection is applied to many applications (e.g., Surveillance System, Security System, Video Coding, Video ROI (region of interest) rate control, Video Conference, information retrieval in video or images). We hope to find a good method of face detection. In this thesis, we proposed a face detection system based on multiple criteria. We use many points of view to consider how to do face detection in color images. We use not only local rectangle features but also global information about faces to construct face classifiers. We also use the human skin color features to decrease false alarms in color images. We introduce our proposed method in detail in this thesis. And we compare our proposed method with a few existent methods. We can know that our proposed method shows higher correct detection rate and lower false alarm rate than other existent methods from comparing table. In conclusion, we proposed a face detection system with higher correct rate and lower false alarm rate in this thesis.
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Chong, Da-Ming, and 崇達明. "The Study on Multiple-Face Detection." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58795654048232255312.

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碩士<br>義守大學<br>資訊管理學系碩士班<br>98<br>Today, images have been ubiquitous, such as the images in television, movies, photos and mobile phones, etc. In a variety of data type images occupy the most storage space than other. When the image data is stored more and more as time, it will take a lot of time to find out an image you want from an image database. Therefore, how to quickly recognize the content of images becomes very important. For the security department, how to utilize human face in image to quickly find out the corresponding data is very important. It is a difficult matter that how to let the computer know whether there are human faces in an image. Because the contents of the image are very complexity, so for recognizing human face in an image, the computers have to do some processes including searching, locating, analyzing, and recognizing processes. The first process of human face recognition is face detection. Due the accuracy of face detection and location can greatly affect the result of face recognition, therefore, how to develop an effective method of face detection is very important. Because multiple-face detection is more difficult than single-face detection, therefore, the goal of this study is how to correctly detect multiple faces in an image. This study utilizes following methods: color segmentation, light compensation, hole technology and the erosion and dilation method of the morphology, to clear non-skin area in an image. This research proposes a scheme of skin reanalysis and uses the statistical features of holes to clear the skin not belonging to human face. After that, using Soble operator and lips-frequency color analysis complete multiple-face detection.
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Hu, Shyuegong, and 胡學恭. "Multiple-Face Detection & Face Recognition for Complex Backgrounds." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/87643401522123211970.

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碩士<br>元智大學<br>資訊管理學系<br>98<br>Human face detection and human face recognition are always popular topics in pattern recognition, they have develop for a long time, and its technique has become more and more robust. At the past, some technique could be restricted by hardware or other experimental settings, so they should made some kind of trade off between detection rate, recognition rate and computing time. Now days, with the development of technology and other equipment some time consuming methods become available. We present a human face detection method which is base on haar-like features and integral image and a human face recognition method which divide human face into different feature area, then use machine learning to divide the feature groups and classify them by these feature information.
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Chang, Shu-How, and 張書豪. "Multiple Human Face Detection and Location in Classroom." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/42210992934746100688.

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碩士<br>國立臺灣師範大學<br>資訊工程研究所<br>98<br>Face detection and human detection are important in all surveillance method applications. In classroom, we can use detection to assist us to observe student activities. Their response will give some suggestions to teacher, and teacher can improve the teaching. Furthermore, it can extend automatically real-time roll call system to help teacher. We propose a new detection method in classroom. Our method employ a combination of AdaBoost classify faces, applied filter and HOG find trustworthy human face. Bubble-Developing Mechanism (BDM) is a similar object tracking method. It’s an easy way to solve the continuous problem in video sequence or live video. Bubble means individual face results in each of frame and they will have weights just like age. Growth over time, bubbles grow old or die. Because BDM have characteristics of time and continuous, it can enhance the performance of our method. In experiment results, improve AdaBoost and applied filters have a better frame rate than original AdaBoost for real-time face detection. BDM can achieve detection rate from 72% to 94% in single person detection and have average 85% detection rate in multiple people environment.
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Yung-Fu, Tsai. "Multiple-pose Face Detection Using Fuzzy C-means Clustering." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200512112000.

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Tsai, Yung-Fu, and 蔡永富. "Multiple-pose Face Detection Using Fuzzy C-means Clustering." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/27909134956224960721.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>93<br>The challenges for face detection from images come from the variation of poses, facial expressions, occlusions, lighting conditions, and so on. We propose a method for multiple-pose face detection from still images. Our proposed method consists of three phases. First, skin pixels are extracted using a skin color model. Connected component analysis is performed to find the skin regions. Second, before extracting the feature vector of a skin region, we apply edge detection to the region. Our feature vector consists of two parts. The first part is obtained by dividing the edge image into 3*4 grids and calculating the number of horizontal edges and the number of vertical edges in each grid. The other part is obtained by computing the summary of color correlogram of the edge image. Third, with a set of training images, the fuzzy c-means (FCM) clustering algorithm is used to build face models. If the Euclidian distance between a feature vector and a face model does not exceed a predefined threshold, the region will be classified to a face. The experimental results show that our method can deal with the variation in poses, rotations, scales, and so on.
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Books on the topic "Multiple face detection"

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Wright, A. G. The Photomultiplier Handbook. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.001.0001.

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This handbook is aimed at helping users of PMTs who are faced with the challenge of designing sensitive light detectors for scientific and industrial purposes. The raison d’être for photomultipliers (PMTs) stems from four intrinsic attributes: large detection area, high, and noiseless gain, and wide bandwidth. Detection involves a conversion process from photons to photoelectrons at the photocathode. Photoelectrons are subsequently collected and increased in number by the action of an incorporated electron multiplier. Photon detection, charge multiplication, and many PMT applications are statistical in nature. For this reason appropriate statistical treatments are provided and derived from first principles. PMTs are characterized by a range of photocathodes offering detection over UV to infra-red wavelengths, the sensitivities of which can be calibrated by National Laboratories. The optical interface between light sources and PMTs, particularly for diffuse or uncollimated light, is sparsely covered in the scientific literature. The theory of light guides, Winston cones, and other light concentrators points to means for optimizing light collection subject to the constraints of Liouville’s theorem (étandue). Certain PMTs can detect single photons but are restricted by the limitations of unwanted background ranging in magnitude from a fraction of a photoelectron equivalent to hundreds of photoelectrons. These sources, together with their correlated nature, are examined in detail. Photomultiplier biasing requires a voltage divider comprising a series of resistors or active components, such as FETs. Correct biasing provides the key to linear operation and so considerable attention is given to the treatment of this topic. Electronic circuits and modules that perform the functions of charge to voltage conversion, pulse shaping, and impedance matching are analysed in detail.
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Book chapters on the topic "Multiple face detection"

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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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Huang, Fuzhen, and Jianbo Su. "Multiple Face Contour Detection Using Adaptive Flows." In Advances in Biometric Person Authentication. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30548-4_16.

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Fröba, Bernhard, and Walter Zink. "On the Combination of Different Template Matching Strategies for Fast Face Detection." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_42.

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Kim, Bumhwi, Sang-Woo Ban, and Minho Lee. "Multiple Occluded Face Detection Based on Binocular Saliency Map." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_79.

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Kumar, Sandeep, Sukhwinder Singh, and Jagdish Kumar. "Multiple Face Detection Using Hybrid Features with SVM Classifier." In Data and Communication Networks. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2254-9_23.

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Nam, Mi Young, and Phill Kyu. "A Face Detection Using Multiple Detectors for External Environment." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_161.

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Lee, Seung-Ik, and Duk-Gyoo Kim. "The Feature Vector Selection for Robust Multiple Face Detection." In Advances in Informatics. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573036_72.

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Javed Mehedi Shamrat, F. M., Zarrin Tasnim, Tahmid Rashik Chowdhury, Rokeya Shema, Md Shihab Uddin, and Zakia Sultana. "Multiple Cascading Algorithms to Evaluate Performance of Face Detection." In Pervasive Computing and Social Networking. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5640-8_8.

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Athira, M., Arun T. Nair, Kesavan Namboothiri, K. S. Haritha, and Nimitha Gopinath. "Multiple Face Detection Tracking and Recognition from Video Sequence." In Intelligent Data Communication Technologies and Internet of Things. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7610-9_26.

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Sharma, Mohit Kumar, Pramod Kumar, P. K. Bharti, and Bikram Patim Bhuyan. "A Review on Multiple Face Detection Techniques and Challenges." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-5231-7_11.

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Conference papers on the topic "Multiple face detection"

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Chen, Oscal Tzyh-Chiang, Wei-Jei Lin, and Cheng-Tai Tsai. "Consistent Learning of Multiple Paths Using Frequency-Aware Clues for Face Forgery Detection." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674230.

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M, Abhineswari, Kurupati Sai Charan, Shrikarti BN, and Sujithra Kanmani R. "Deep Fake Detection using Transfer Learning: A Comparative study of Multiple Neural Networks." In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2024. http://dx.doi.org/10.1109/iconscept61884.2024.10627869.

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Archana, T., T. Venugopal, and M. Praneeth Kumar. "Multiple face detection in color images." In 2015 International Conference on Signal Processing And Communication Engineering Systems (SPACES). IEEE, 2015. http://dx.doi.org/10.1109/spaces.2015.7058220.

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Chaudhuri, Bindita, Noranart Vesdapunt, and Baoyuan Wang. "Joint Face Detection and Facial Motion Retargeting for Multiple Faces." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00995.

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Filali, Hajar, Jamal Riffi, Adnane Mohamed Mahraz, and Hamid Tairi. "Multiple face detection based on machine learning." In 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2018. http://dx.doi.org/10.1109/isacv.2018.8354058.

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Abin, Ahmad Ali, Mehran Fotouhi, and Shohreh Kasaei. "Real-time multiple face detection and tracking." In 2009 14th International CSI Computer Conference (CSICC 2009) (Postponed from July 2009). IEEE, 2009. http://dx.doi.org/10.1109/csicc.2009.5349610.

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"PARTS-BASED FACE DETECTION AT MULTIPLE VIEWS." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002062202980301.

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Angadi, Shanmukhappa A., and Vishwanath C. Kagawade. "Detection of Face Spoofing using Multiple Texture Descriptors." In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE, 2018. http://dx.doi.org/10.1109/ctems.2018.8769129.

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Sun, Bo, Siming Cao, Jun He, and Lejun Yu. "Face detection based on multiple kernel learning algorithm." In SPIE Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2016. http://dx.doi.org/10.1117/12.2235837.

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Li, Jingjing, Xinfeng Zhang, Yongbing Zhang, Haoqian Wang, and Fang Yang. "Face Liveness Detection Based On Multiple Feature Descriptors." In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959844.

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Reports on the topic "Multiple face detection"

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Mishra, Shivangi, Steve Crookes, and Srijak Bhatnagar. Muskoxen and Genomics in the Community (MAGIC) Workshop: A Detailed Report. Arctic Institute of North America, 2025. https://doi.org/10.33174/aina2025tr04magicreport.

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This report provides an overview and summary of the Muskoxen and Genomics in the Community (MAGIC) Workshop held in Cambridge Bay, Nunavut in January 2024. The workshop brought together Inuit Knowledge Holders, hunters, and decision makers, scientists from a variety of disciplines and international participants. Together the group considered Inuit knowledge of and priorities for muskoxen, scientific knowledge and gaps, and the potential for genomic and DNA-based tools to help secure a viable future for muskoxen in the face of multiple climate-related stressors. Indigenous Knowledge combined with application of novel scientific advances, such as whole genome sequencing (WGS) of target organisms and use of DNA-based molecular ecological tools (such as environmental DNA markers for species detection from the soup of DNA molecules found within environmental samples), and consideration of the broader ecosystem offers the opportunity to change the way in which healthy wildlife populations are sustained.
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Jung, Carina, Karl Indest, Matthew Carr, Richard Lance, Lyndsay Carrigee, and Kayla Clark. Properties and detectability of rogue synthetic biology (SynBio) products in complex matrices. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45345.

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Synthetic biology (SynBio) aims to rationally engineer or modify traits of an organism or integrate the behaviors of multiple organisms into a singular functional organism through advanced genetic engineering techniques. One objective of this research was to determine the environmental persistence of engineered DNA in the environment. To accomplish this goal, the environmental persistence of legacy engineered DNA building blocks were targeted that laid the foundation for SynBio product development and application giving rise to “post-use products.” These building blocks include genetic constructs such as cloning and expression vectors, promoter/terminator elements, selectable markers, reporter genes, and multi-cloning sites. Shotgun sequencing of total DNA from water samples of pristine sites was performed and resultant sequence data mined for frequency of legacy recombinant DNA signatures. Another objective was to understand the fate of a standardized contemporary synthetic genetic construct (SC) in the context of various chassis systems/genetic configurations representing different degrees of “genetic bioavailability” to the environmental landscape. These studies were carried out using microcosms representing different environmental matrices (soils, waters, wastewater treatment plant (WWTP) liquor) and employed a novel genetic reporter system based on volatile organic compounds (VOC) detection to assess proliferation and persistence of the SC in the matrix over time.
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Delwiche, Michael, Boaz Zion, Robert BonDurant, Judith Rishpon, Ephraim Maltz, and Miriam Rosenberg. Biosensors for On-Line Measurement of Reproductive Hormones and Milk Proteins to Improve Dairy Herd Management. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7573998.bard.

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The original objectives of this research project were to: (1) develop immunoassays, photometric sensors, and electrochemical sensors for real-time measurement of progesterone and estradiol in milk, (2) develop biosensors for measurement of caseins in milk, and (3) integrate and adapt these sensor technologies to create an automated electronic sensing system for operation in dairy parlors during milking. The overall direction of research was not changed, although the work was expanded to include other milk components such as urea and lactose. A second generation biosensor for on-line measurement of bovine progesterone was designed and tested. Anti-progesterone antibody was coated on small disks of nitrocellulose membrane, which were inserted in the reaction chamber prior to testing, and a real-time assay was developed. The biosensor was designed using micropumps and valves under computer control, and assayed fluid volumes on the order of 1 ml. An automated sampler was designed to draw a test volume of milk from the long milk tube using a 4-way pinch valve. The system could execute a measurement cycle in about 10 min. Progesterone could be measured at concentrations low enough to distinguish luteal-phase from follicular-phase cows. The potential of the sensor to detect actual ovulatory events was compared with standard methods of estrus detection, including human observation and an activity monitor. The biosensor correctly identified all ovulatory events during its testperiod, but the variability at low progesterone concentrations triggered some false positives. Direct on-line measurement and intelligent interpretation of reproductive hormone profiles offers the potential for substantial improvement in reproductive management. A simple potentiometric method for measurement of milk protein was developed and tested. The method was based on the fact that proteins bind iodine. When proteins are added to a solution of the redox couple iodine/iodide (I-I2), the concentration of free iodine is changed and, as a consequence, the potential between two electrodes immersed in the solution is changed. The method worked well with analytical casein solutions and accurately measured concentrations of analytical caseins added to fresh milk. When tested with actual milk samples, the correlation between the sensor readings and the reference lab results (of both total proteins and casein content) was inferior to that of analytical casein. A number of different technologies were explored for the analysis of milk urea, and a manometric technique was selected for the final design. In the new sensor, urea in the sample was hydrolyzed to ammonium and carbonate by the enzyme urease, and subsequent shaking of the sample with citric acid in a sealed cell allowed urea to be estimated as a change in partial pressure of carbon dioxide. The pressure change in the cell was measured with a miniature piezoresistive pressure sensor, and effects of background dissolved gases and vapor pressures were corrected for by repeating the measurement of pressure developed in the sample without the addition of urease. Results were accurate in the physiological range of milk, the assay was faster than the typical milking period, and no toxic reagents were required. A sampling device was designed and built to passively draw milk from the long milk tube in the parlor. An electrochemical sensor for lactose was developed starting with a three-cascaded-enzyme sensor, evolving into two enzymes and CO2[Fe (CN)6] as a mediator, and then into a microflow injection system using poly-osmium modified screen-printed electrodes. The sensor was designed to serve multiple milking positions, using a manifold valve, a sampling valve, and two pumps. Disposable screen-printed electrodes with enzymatic membranes were used. The sensor was optimized for electrode coating components, flow rate, pH, and sample size, and the results correlated well (r2= 0.967) with known lactose concentrations.
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