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Journal articles on the topic 'Face detection'
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Hajiarbabi, Mohammadreza, and Arvin Agah. "Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images." International Journal of Computer Vision and Image Processing 5, no. 2 (2015): 35–57. http://dx.doi.org/10.4018/ijcvip.2015070103.
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Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.
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Liu, Linrunjia, Gaoshuai Wang, and Qiguang Miao. "ADYOLOv5-Face: An Enhanced YOLO-Based Face Detector for Small Target Faces." Electronics 13, no. 21 (2024): 4184. http://dx.doi.org/10.3390/electronics13214184.
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Benefiting from advancements in generic object detectors, significant progress has been achieved in the field of face detection. Among these algorithms, the You Only Look Once (YOLO) series plays an important role due to its low training computation cost. However, we have observed that face detectors based on lightweight YOLO models struggle with accurately detecting small faces. This is because they preserve more semantic information for large faces while compromising the detailed information for small faces. To address this issue, this study makes two contributions to enhance detection performance, particularly for small faces: (1) modifying the neck part of the architecture by integrating a Gather-and-Distribute mechanism instead of the traditional Feature Pyramid Network to tackle the information fusion challenges inherent in YOLO-based models; and (2) incorporating an additional detection head specifically designed for detecting small faces. To evaluate the performance of the proposed face detector, we introduce a new dataset named XD-Face for the face detection task. In the experimental section, the proposed model is trained using the Wider Face dataset and evaluated on both Wider Face and XD-face datasets. Experimental results demonstrate that the proposed face detector outperforms other excellent face detectors across all datasets involving small faces and achieved improvements of 1.1%, 1.09%, and 1.35% in the AP50 metric on the WiderFace validation dataset compared to the baseline YOLOv5s-based face detector.
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Anjali, Muneshwar, and Vattam Prof.Jayarajesh. "Face Detection System with Face Recognition." International Organization of Research & Development (IORD) 9, no. 1 (2021): 5. https://doi.org/10.5281/zenodo.5016190.
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The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses the personal characteristics of a person to identify the person's identity. The human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognizes a face as individuals. The stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition patterns, namely, the Eigenface method and the Fisherface method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigenfaces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project's face detection system with face recognition is Image processing. The software required for this project is Matlab software.
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Nam, Amir Nobahar Sadeghi. "Face Detection." Volume 5 - 2020, Issue 9 - September 5, no. 9 (2020): 688–92. http://dx.doi.org/10.38124/ijisrt20sep391.
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Face detection is one of the challenging problems in the image processing, as a main part of automatic face recognition. Employing the color and image segmentation procedures, a simple and effective algorithm is presented to detect human faces on the input image. To evaluate the performance, the results of the proposed methodology is compared with ViolaJones face detection method.
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Lewis, Michael B., and Andrew J. Edmonds. "Face Detection: Mapping Human Performance." Perception 32, no. 8 (2003): 903–20. http://dx.doi.org/10.1068/p5007.
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The recognition of faces has been the focus of an extensive body of research, whereas the preliminary and prerequisite task of detecting a face has received limited attention from psychologists. Four experiments are reported that address the question how we detect a face. Experiment 1 reveals that we use information from the scene to aid detection. In experiment 2 we investigated which features of a face speed the detection of faces. Experiment 3 revealed inversion effects and an interaction between the effects of blurring and reduction of contrast. In experiment 4 the sizes of effects of reversal of orientation, luminance, and hue were compared. Luminance was found to have the greatest effect on reaction time to detect faces. The results are interpreted as suggesting that face detection proceeds by a pre-attentive stage that identifies possible face regions, which is followed by a focused-attention stage that employs a deformable template. Comparisons are drawn with automatic face-detection systems.
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Omaima, N. A. AL-Allaf. "Review of Face Detection Systems Based Artificial Neural Networks Algorithms." International Journal of Multimedia & Its Applications (IJMA) 6, no. 1 (2021): 1–16. https://doi.org/10.5281/zenodo.4730130.
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Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.
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Nidhi, Soni* Priya Mate. "FACE DETECTION AND RECOGNIZATION USING PCA ALGORITHM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 5 (2017): 717–21. https://doi.org/10.5281/zenodo.801247.
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Image databases and live video data is growing rapidly, their intelligent or automatic examining is becoming exceptionally more important. Human faces are one of very common and very particular objects that we need to try to detect in images. Face detection is very difficult task in image analysis which has each day many applications. We can illustrate the face detection problem as a computer vision task which involve in detecting one or several human faces in an image. Identification & Authentication has become major problems in present digital world. Face detection plays a significant role in identification & authentication. In this paper we propose mechanism for detecting faces from low luminance video and poor quality video files using PCA and Voila Jones Algorithm. We show effectiveness of our algorithm by taking low light video and poor quality video for comparison.
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Nidhi, Soni, and Mate2 Priya. "FACE DETECTION AND RECOGNIZATION USING PCA ALGORITHM." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 6 (2017): 21–25. https://doi.org/10.5281/zenodo.802177.
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Image databases and live video data is growing rapidly, their intelligent or automatic examining is becoming exceptionally more important. Human faces are one of very common and very particular objects that we need to try to detect in images. Face detection is very difficult task in image analysis which has each day many applications. We can illustrate the face detection problem as a computer vision task which involve in detecting one or several human faces in an image. Identification & Authentication has become major problems in present digital world. Face detection plays a significant role in identification & authentication. In this paper we propose mechanism for detecting faces from low luminance video and poor quality video files using PCA and Voila Jones Algorithm. We show effectiveness of our algorithm by taking low light video and poor quality video for comparison
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E, Subash, Hariprasath M, Aathithya S, et al. "Attendance Management System Using Face Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43195.
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This paper presents a Hybrid Multi-Stage Face Detection Algorithm that integrates traditional and deep learning methods for improved accuracy and efficiency. The process begins with Preprocessing and Enhancement to refine image quality. Fast Face Candidate Selection (Haar + HOG + SVM) quickly detects potential faces, followed by Precise Localization using MTCNN to refine detections and extract facial landmarks. Deep Learning Verification (RetinaFace/YOLO) eliminates false positives, ensuring reliability. Finally, Face Tracking (Kalman Filter + SORT) maintains consistency in video streams. This approach provides a robust and adaptable solution for real-world face detection applications. Keywords : Face Detection, Hybrid Algorithm, Deep Learning, Haar Cascade, HOG + SVM, MTCNN, RentinaFace, YOLO, Face Tracking, Real-Time Processing
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Hayashi, Shinji, and Osamu Hasegawa. "Robust Face Detection for Low-Resolution Images." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 1 (2006): 93–101. http://dx.doi.org/10.20965/jaciii.2006.p0093.
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Face detection, one of the most actively researched and progressive computer vision fields, has been little studied in low-resolution images. Using the AdaBoost-based face detector and MIT+CMU frontal face test set – the standard detector and images for evaluation in face detection – we found that face detection rate falls to 39% from 88% as face resolution decreases from 24×24 pixels to 6×6 pixels. We discuss a proposal using “portrait images,” “image expansion,” “frequency-band limitation of features” and “two-detector integration” and show that 71% of face detection rate is obtained for 6×6 pixel faces of MIT+CMU frontal face test set. Note that each of the above detections involves 100 false positives for 112 evaluation images.
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Patil, Vaibhavi, Sakshi Patil, Krishna Ganjegi, and Pallavi Chandratre. "Face and Eye Detection for Interpreting Malpractices in Examination Hall." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1119–23. http://dx.doi.org/10.22214/ijraset.2022.41456.
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Abstract: One of the most difficult problems in computer vision is detecting faces and eyes. The purpose of this work is to give a review of the available literature on face and eye detection, as well as assessment of gaze. With the growing popularity of systems based on face and eye detection in a range of disciplines in recent years, academia and industry have paid close attention to this topic. Face and eye identification has been the subject of numerous investigations. Face and eye detection systems have made significant process despite numerous challenges such as varying illumination conditions, wearing glasses, having facial hair or moustache on the face, and varying orientation poses or occlusion of the face. We categorize face detection models and look at basic face detection methods in this paper. We categorize face detection models and look at basic face detection methos in this paper. Then we’ll go through eye detection and estimation techniques. Keywords: Image Processing, Face Detection, Eye Detection, Gaze Estimation
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Zhang, Ruifang, Bohan Deng, Xiaohui Cheng, and Hong Zhao. "GCS-YOLOv8: A Lightweight Face Extractor to Assist Deepfake Detection." Sensors 24, no. 21 (2024): 6781. http://dx.doi.org/10.3390/s24216781.
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To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model’s parameters, thereby lightening the network. Additionally, we add a new P6 large target detection layer to expand the receptive field and capture multi-scale features, solving the problem of detecting large-scale faces in low-compression deepfake videos. We also design a cross-scale feature fusion module called CCFG (CNN-based Cross-Scale Feature Fusion with GDConv), which integrates features from different scales to enhance the model’s adaptability to scale variations while reducing network parameters, addressing the high computational resource requirements of traditional face extractors. Furthermore, we improve the detection head by utilizing group normalization and shared convolution, simplifying the process of face detection while maintaining detection performance. The training dataset was also refined by removing low-accuracy and low-resolution labels, which reduced the false detection rate. Experimental results demonstrate that, compared to YOLOv8, this face extractor achieves the AP of 0.942, 0.927, and 0.812 on the WiderFace dataset’s Easy, Medium, and Hard subsets, representing improvements of 1.1%, 1.3%, and 3.7% respectively. The model’s parameters and FLOPs are only 1.68 MB and 3.5 G, reflecting reductions of 44.2% and 56.8%, making it more effective and lightweight in extracting faces from deepfake videos.
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Hashim, Siti, and Paul Mccullagh. "Face detection by using Haar Cascade Classifier." Wasit Journal of Computer and Mathematics Science 2, no. 1 (2023): 1–8. http://dx.doi.org/10.31185/wjcm.109.
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the Haar Cascade Classifier is a popular technique for object detection that uses a machine-learning approach to identify objects in images and videos. In the context of face detection, the algorithm uses a series of classifiers that are trained on thousands of positive and negative images to identify regions of the image that may contain a face. The algorithm is a multi-stage process that involves collecting training data, extracting features, training the classifiers, building the cascade classifier, detecting faces in the test image, and post-processing the results to remove false positives and false negatives. The algorithm has been shown to be highly accurate and efficient for detecting faces in images and videos, but it has some limitations, including difficulty in detecting faces under challenging lighting conditions or when the faces are partially occluded. Overall, the Haar Cascade Classifier algorithm remains a powerful and widely-used tool for face detection, but it is important to carefully evaluate its performance in the specific context of each application and consider using more advanced techniques when necessary.
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Hsieh, Chen-Chiung, and Jun-An Lai. "Face Mole Detection, Classification and Application." Journal of Computers 10, no. 1 (2015): 12–23. http://dx.doi.org/10.17706/jcp.10.1.12-23.
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Hire, Ms A. N., and Prof Dr M. P. Satone. "A Review on Face Detection Techniques." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 1470–76. http://dx.doi.org/10.31142/ijtsrd14107.
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S.V, Viraktamath, Mukund Katti, Aditya Khatawkar, and Pavan Kulkarni. "Face Detection and Tracking using OpenCV." SIJ Transactions on Computer Networks & Communication Engineering 04, no. 03 (2016): 01–06. http://dx.doi.org/10.9756/sijcnce/v4i3/0103540102.
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R Maale, Bhavana, and Suvarna Nandyal. "Face Detection Using Haar Cascade Classifiers." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 1179–82. https://doi.org/10.21275/sr21306204717.
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Mitra Anuj Chaudhary, Aditya. "Parallelization of Face Detection using OpenMP." International Journal of Science and Research (IJSR) 12, no. 7 (2023): 505–10. http://dx.doi.org/10.21275/sr23707233420.
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Savitha, A. C., Kumar KM Madhu, M. Pallavi, Chincholi Pallavi, H. B. Prethi, and Rachitha. "Experimental Detection of Deep Fake Images Using Face Swap Algorithm." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 56–61. https://doi.org/10.5281/zenodo.15397033.
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Deepfakes enable highly realistic face-swapping in videos using deep learning. To address the threat posed by Deepfakes, the DFDC dataset, the largest face-swapped video dataset to date, was created with over 100,000 clips generated using multiple methods, including Deepfake Autoencoders and GANs. The dataset consists of videos from 3,426 consenting actors. It supports the development of scalable Deepfake detection models and includes a public Kaggle competition to benchmark solutions. The dataset highlights the complexity of Deepfake detection but shows the potential for generalization to real-world scenarios. Deepfake creation using Generative Adversarial Networks (GANs) has grown rapidly, producing highly realistic fake images. This paper introduces a new detection method focused on analyzing the convolutional traces left by the GAN generation process. Using an Expectation Maximization (EM) algorithm, the approach extracts local features that reveal forensic traces in images. Validation was performed against five GAN architectures (GDWCT, STARGAN, ATTGAN, STYLEGAN, STYLEGAN2) using the CELEBA dataset. The results demonstrate the method's effectiveness in detecting Deepfakes and its potential for forensic investigations by identifying the generation process. We choose the Fake-or-Real dataset, which is the most recent benchmark dataset. The dataset was created with a text-to-speech model and is divided into four sub-datasets: for-rece, for-2-sec, for-norm and for-original. These datasets are classified into sub-datasets mentioned above according to audio length and bit rate. The experimental results show that the support vector machine (SVM) outperformed the other machine learning (ML) models in terms of accuracy on for-rece and for-2-sec datasets, while the gradient boosting model performed very well using for-norm dataset. The VGG-16 model produced highly encouraging results when applied to the for-original dataset. The VGG-16 model outperforms other state-of-the-art approaches. Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a super realistic form, in which it is difficult to distinguish between real and fake features.
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Vizilter, Yu V., V. S. Gorbatsevich, and A. S. Moiseenko. "SINGLE-SHOT FACE DETECTION AND RECOGNITION BASED ON CNN." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 202 (April 2021): 11–20. http://dx.doi.org/10.14489/vkit.2021.04.pp.011-020.
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The paper proposes an architecture and training method of a deep convolutional neural network for simultaneous face detection and recognition. The implemented approach combines the ideas of SSD (Single Shot Detector) and Faster R-CNN (Region proposal Convolutional Neural Networks) algorithms. Face detection is performed similarly to single-stage detection algorithms, and then a biometric template is built by employing RoI (Region of Interest) pooling layers and using the separate branch of the neural network. Training process includes three stages: pretraining of thebasic CNN for face recognition on face images, fine-tuning by using RoI pooling on in painted face images, adding SSD layers and fine-tuning on face detection. Wherein, at the latter stage, training is performed by using shared layers technology for two databases simultaneously. The main feature of the algorithm is high processing speed, which does not depend on the number of faces in the input image. For example, in case of using ResNet-34 as the core architecture for the algorithm, the required time for detecting faces and building biometric templates on an image with 100 faces is less than 13 ms. For training purposes we use CASIA-WebFace for face recognition task and Wider Face for face detection task. Testing is performed on FDDB (Face Detection Dataset and Benchmark), since this database is closer to practical applications than Wider. As long as the main practical task the developed method is intended for is face reidentification, we use Fei Face DataBase for face recognition quality testing. We obtain TPR (True Positive Rate) = 0.928@1000 on FDDB Face DataBase and FAR (Face Acceptance Rate) = 0.03309@FRR (Face Rejection Rate) = 10–4. Therefore, the proposed algorithm allows solving face detection and reidentification tasks in real time with any number of faces on an input image.
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Sakshi, Chabra, and V.S Pandey Dr. "REAL TIME FACE FEATURE EXTRACTION AND RECOGNITION USING ROBUST ALGORITHM." International Journal of Advances in Engineering & Scientific Research Vol.4, Issue 3, May-2017 (2017): pp 27–29. https://doi.org/10.5281/zenodo.801667.
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Human face detection and recognition play important roles in many applications such as video surveillance and face image database management. In our project, we have studied worked on both face recognition and detection techniques and developed algorithms for them. In face recognition the algorithm used is Robust in which we recognize an unknown test image by comparing it with the known training images stored in the database as well as give information regarding the person recognized. These techniques works well under robust conditions like complex background, different face positions. These algorithms give different rates of accuracy under different conditions as experimentally observed. In face detection, we have developed an algorithm that can detect human faces from an image. We have taken skin colour as a tool for detection. This technique works well for Indian faces which have a specific complexion varying under certain range. We have taken real life examples and simulated the algorithms in MATLAB successfully.Theresearcher addressed the problem of automated face recognition by functionally dividing it into face detection and face recognition. Different approaches to the problems of face detection and face recognition were evaluated, and five systems were proposed and implemented using the Matlab technical computing language. In the implemented frontal-view face detection systems, automated face detection was achieved using a deformable template algorithm based on image invariants. The deformable template was implemented with a perceptron. Unsupervised learning using Kohonen Feature Maps was used to create the Perceptron's A-units. The natural symmetry of faces was utilised to improve the efficiency of the face detection model. The deformable template was run down the line of symmetry of the face in search of the exact face location.
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Rorimpandey, Gladly C., Sondakh Agnes Intan, and Quido C. Kainde. "APPLICATION OF MULTI-TASK CASCADED CONVOLUTIONAL NEURAL NETWORK ALGORITHM IN SCHOOL SUPERVISOR ATTENDANCE SYSTEMS IN THE FIELD OF COMPUTER VISION." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 593–600. https://doi.org/10.52436/1.jutif.2024.5.4.2218.
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The attendance system used by the Education Department can be said to be still manual. Where they use the Timestamp application to take photos. Where the application only takes faces without detecting the face. Therefore, researchers created a face detection presence system by applying the Multi-Task Cascaded Convolutional Neural Network algorithm using the face-api.min.js library for the face detection process. The aim of this research is to make it easier for school supervisors to manage attendance, so they can provide accurate information. Then, based on the research results, a face detection and location detection system for school supervisors was successfully developed using the Multi-Task Cascaded Convolutional Neural Network (MTCNN) algorithm. From the results of tests carried out using a dataset of 140 images from 28 people with different photos taken (face view, top view, bottom view, left side view, right side view). Test results on the facial presence detection system using the MTCNN (Multi-Task Cascaded Convolutional Neural Network) algorithm succeeded in detecting faces by 100%.
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Gosavi, Prof Amol. "Deepfake Video Face Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5840–47. https://doi.org/10.22214/ijraset.2025.69233.
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The emergence of deepfake technology, which relies on generative adversarial networks (GANs), has raised substantial concerns in the realm of digital media. This technology enables the manipulation of facial features in videos, leading to potential misuse for spreading false information, misrepresentation, and identity theft. As a result, there is a pressing need to establish robust methods for detecting deepfakes effectively. Detecting deepfake videos is particularly difficult due to their increasingly realistic appearance and the sophisticated techniques involved in their creation. This research introduces an innovative approach to deepfake detection that leverages advanced deep learning methodologies. Specifically, the study employs Convolutional Neural Networks (CNNs) in combination with Recurrent Neural Networks (RNNs), with a particular focus on Long Short-Term Memory (LSTM) networks, to enhance the identification process for deepfake content. The proposed model is trained on comprehensive datasets, including FaceForensics++ and the Deepfake Detection Challenge (DFDC). To bolster detection accuracy, the methodology includes a pre-processing pipeline that not only reduces the frame rates of video inputs but also isolates and focuses on facial regions using Haar Cascade classifiers. This dual approach of analyzing both spatial and temporal inconsistencies within video frames contributes significantly to the overall effectiveness of deepfake detection. Through rigorous testing, the proposed method has demonstrated a high level of accuracy in distinguishing between authentic and manipulated videos, showcasing its potential as a reliable solution in the ongoing fight against digital media fraud. It is crucial for researchers and practitioners in the field of video forensics and digital media security to further explore and refine such advanced detection techniques
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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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Tolha Baig, Mohammed, and Dr Nethravathi B. "A DEEP LEARNING STRATEGY FOR EFFECTIVELY DETECTING SMALL FACES IN CHALLENGING IMAGES." International Journal of Engineering Applied Sciences and Technology 09, no. 01 (2024): 64–70. http://dx.doi.org/10.33564/ijeast.2024.v09i01.008.
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This paper introduces the RetinaNet baseline, a single-stage face detector aimed at overcoming the challenges faced by traditional facial detection methods. Leveraging deep learning techniques, the model demonstrates significant improvements in accuracy and speed, particularly in detecting small, occluded, or blurred faces. Through experiments on datasets like WIDER FACE and FDDB, the proposed method achieves impressive average precision scores, outperforming other one-stage detectors. Trained using the PyTorch framework, the model exhibits a high accuracy rate of 95.6% for successfully detected faces. Overall, this research contributes to advancing facial detection by offering an efficient solution capable of handling realworld scenarios, with potential applications in security, surveillance, and human-computer interaction.
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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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Akash, Chaudhary, AnkitaSingh, and Km.Yachana. "Anti Spoofing Face Detection with Convolutional Neural Networks Classifier." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 745–50. https://doi.org/10.5281/zenodo.7953326.
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The ability to detect spoofed faces has become a critical concern in various applications, such as face recognition systems, banking, and security measures. Thisresearchpresentsa simple system that can detect whether a facein video stream is spoofed or real using pre-trained models for face detection and anti-spoofing. The system uses a continuous loop to read each frame of the video stream, to assess whether a face image is real or spoof, first detect faces using the pre-trained face detection model, then crop and resize the face image. If the model predicts that the face is fake, the system draws a red rectangle around the face and displays the label "spoof." If the model predicts that the face is real, the system draws a green rectangle around the face and displays the label "real." The proposed system achieved a high accuracy rate in detecting spoofed faces, making it suitable for real-world applications.
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Mamieva, Dilnoza, Akmalbek Bobomirzaevich Abdusalomov, Mukhriddin Mukhiddinov, and Taeg Keun Whangbo. "Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach." Sensors 23, no. 1 (2023): 502. http://dx.doi.org/10.3390/s23010502.
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Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations. However, deep learning’s quick development in computer vision has also sped up the development of a number of deep learning-based face detection frameworks, many of which have significantly improved accuracy in recent years. When detecting faces in face detection software, the difficulty of detecting small, scale, position, occlusion, blurring, and partially occluded faces in uncontrolled conditions is one of the problems of face identification that has been explored for many years but has not yet been entirely resolved. In this paper, we propose Retina net baseline, a single-stage face detector, to handle the challenging face detection problem. We made network improvements that boosted detection speed and accuracy. In Experiments, we used two popular datasets, such as WIDER FACE and FDDB. Specifically, on the WIDER FACE benchmark, our proposed method achieves AP of 41.0 at speed of 11.8 FPS with a single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are results among one-stage detectors. Then, we trained our model during the implementation using the PyTorch framework, which provided an accuracy of 95.6% for the faces, which are successfully detected. Visible experimental results show that our proposed model outperforms seamless detection and recognition results achieved using performance evaluation matrices.
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Bhange, Prof Anup. "Face Detection System with Face Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1095–100. http://dx.doi.org/10.22214/ijraset.2022.39976.
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Abstract: The face is one of the easiest way to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Now a days Human Face Detection and Recognition become a major field of interest in current research because there is no deterministic algorithm to find faces in a given image. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is recognition, which recognize (by comparing face with picture or either with image captured through webcam) a face as an individual. In face detection and recognition technology, it is mainly introduced from the OpenCV method. Face recognition is one of the much-studied biometrics technology and developed by experts. The area of this project face detection system with face recognition is Image processing. The software requirement for this project is Python. Keywords: face detection, face recognition, cascade_classifier, LBPH.
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Zhu, Y., and F. Cutu. "Face Detection using Half-Face Templates." Journal of Vision 3, no. 9 (2010): 839. http://dx.doi.org/10.1167/3.9.839.
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Robertson, David J., Rob Jenkins, and A. Mike Burton. "Face detection dissociates from face identification." Visual Cognition 25, no. 7-8 (2017): 740–48. http://dx.doi.org/10.1080/13506285.2017.1327465.
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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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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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Alharbey, Riad, Ameen Banjar, Yahia Said, Mohamed Atri, and Mohamed Abid. "A Human Face Detector for Big Data Analysis of Pilgrim Flow Rates in Hajj and Umrah." Engineering, Technology & Applied Science Research 14, no. 1 (2024): 12861–68. http://dx.doi.org/10.48084/etasr.6668.
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In today's digital world, some crowded venues still rely on outdated methods, such as counting people using counters or sensors at the entrance. These techniques generally fail in areas where people move randomly. Crowd management is an important challenge for ensuring human safety. This paper focuses on developing a crowd management system for Hajj and Umrah duty. Motivated by the recent artificial intelligence techniques and the availability of large-scale data, a crowd management system was established and is presented in this paper. Utilizing the most recent Deep Learning techniques, the proposed crowd management system will be charged with detecting human faces, face identification, tracking, and human face counting tasks. Face counting and detection will be achieved by computing the number of people in a given area. Face detection and tracking will be carried out for person identification, flow rate estimation, and security. The suggested crowd management system is composed of three key components: (1) face detection, (2) assignment of a specific identifier (ID) to each detected face, (3) each detected face will be compared to the stored faces in the dataset. If the detected face is identified, it will be assigned to its ID, or a new ID will be assigned. The crowd management system has been developed to improve the Cross-Stage Partial Network (CSPNet) with attention module integration. An attention module was employed to address object location challenges and a channel-wise attention module for determining the objects of focus. Extensive experiments on the WIDER FACE dataset proved the robustness of the proposed face detection module, which allows for building reliable crowd management and flow rate estimation systems through detecting, tracking, and counting human faces. The reported results demonstrated the power of the proposed method while achieving high detection performance in terms of processing speed and detection accuracy.
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Garg, Aakansh, Abhinav Dagar, Chetanya Khurana, Dr Rajesh Singh, and Ms Kalpana Anshu. "Face Mask Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3179–85. http://dx.doi.org/10.22214/ijraset.2022.42947.
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Abstract: Various industries around the world were affected by the COVID-19 pandemic. Some sectors, like the development industry, have remained open despite the closures. The WHO has issued a warning for workers to wear a mask and avoid working in areas with high risks of infection. This paper developed a computing system that will automatically detect the presence of masks among workers on construction sites during the onset of the pandemic. It collected over a thousand images and added them to a database. The algorithm was trained and tested on various object detection models. It had been then ready to detect the presence of individuals using the Faster R-CNN Inception V2. The space between people was computed using the Euclidian distance. The model was then trained on various pictures and videos to spot the presence of masks.
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Popereshnyak, S. V., R. O. Skoryk, D. V. Kuptsov, and R. V. Kravchenko. "Human face recognition system in video stream." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 296–304. https://doi.org/10.15407/pp2024.02-03.296.
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In the work, an analysis of detection methods and faces in the video stream and their effectiveness in real time was carried out. Modern algorithms and pre-trained models have been found to be able to recognize faces with high accuracy, but their significant drawback is, in particular, vulnerability to attacks using fake faces. Therefore, the work also analyzed approaches to detecting living faces and the possibility of their implementation in the system. Using an object-oriented approach, a tool for face capture, receiving a video stream from various sources, detecting unknown and previously captured faces in a video stream, and recognizing live faces was designed and developed. The system has been adapted to work in real time using the GPU. The work improved the architecture of a convolutional neural network for recognizing living faces with the creation of a dataset from a combination of own footage and open datasets. Also, a user interface for the face recognition system was developed. The work improved identification procedures and simplified detection of persons on video for employees of the security department of enterprises by implementing liveness detection face recognition methods. As a result of the research, a system was designed, which is intended for detection, recognition and detection of living faces in a video stream. After analyzing the known successful software products, niches that need a new solution were identified. Based on them, functional and non-functional requirements were developed. The process of recognizing faces in the video stream has been modified by implementing our own Liveness Detection model.
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Asni b, Andi, and Tamara Octa Dana. "Identifikasi Wajah Dengan Segmentasi Warna Kulit Menggunakan Metode Viola Jones." Jurnal Teknik Elektro Uniba (JTE Uniba) 4, no. 1 (2019): 1–6. http://dx.doi.org/10.36277/jteuniba.v4i1.47.
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Abstract - Face detection (face detection) is one of the initial steps that is very important before the face recognition process (face recognition). Face detection is the detection of objects in the form of faces in which there are special features that represent the shape of faces in general. One method of face detection is the Viola Jones method. Viola Jones method is used to detect faces and skin color segmentation, test data processing using Matlab and capture on a Smartphone. The test is carried out at normal light intensity with a predetermined distance and face position. The results of this study indicate the level of accuracy of detection of face image variations in the position of face images facing forward (frontal), sideways left and right 45̊. But it has a weakness of this face detection system that is unable to determine faces in images that have faces that are not upright (tilted) or not frontal (facing sideways) at a 90̊ angle. Face position that is upright / not upright will determine the success of this face detection. The level of identification of the Viola Jones simulation was 100% with 4 images consisting of 3 boys and 1 girl.
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C.G., Ezekwe I. C. Ituma P. I. Okwu. "FACE PROCESSING AND RECOGNITION BASED CLASSROOM ATTENDANCE SYSTEM." Global Journal of Engineering Science and Research Management 5, no. 4 (2018): 12–23. https://doi.org/10.5281/zenodo.1222126.
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Educational institutions’ administrators in our country and the whole world are concerned about regularity of student attendance. Student overall academic performance is affected by it. The conventional method of taking attendance by calling names or signing on paper is very time consuming, and hence inefficient. This problem gave birth to research on Radio frequency identification (RFID) authentication with face processing and recognition though in this paper we basically highlighted on the face processing and recognition. The system is made up of a camera which take the photos of individuals and a computer unit which performs face detection (locating faces from the image removing the background information) and face recognition (identifying the persons) First, face images are acquired using webcam to create the database. Face recognition system will detect the location of face in the image and will extract the features from the detected faces. As a result of feature extraction process, templates or eigenfaces are generated which are reduced set of data that represents the unique features of enrolled user’s face. These templates are stored in database after eigenface calculation. The basis of the eigenfaces calculation in this work is the Principal Component Analysis (PCA). The Principal Component Analysis is a method of projection to a subspace and is widely used in pattern recognition. The objectives of PCA are the replacement of correlated vectors of large dimensions with the uncorrelated vectors of smaller dimensions and to calculate a basis for the data set. C# was used for serial communication, the image training, detection and recognition and for the application interfaces, and in connection other physical components. At the end of this research work, we were able to achieve a classroom attendance system that uses the students’ images for authentication and at the same time, it is able to have high level of security and privacy because another student can never take attendance for the other.
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Telugu, Maddileti, Shriphad Rao G., Sai Madhav Vaddemani, and Sharan Ganti. "Home Security using Face Recognition Technology." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 678–82. https://doi.org/10.35940/ijeat.B3917.129219.
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Face is the easiest way to penetrate each other's personal identity. Face recognition is a method of personal identification using the personal characteristics of an individual to decide the identification of a person. The method of human face recognition consists basically of two levels, namely face detection and face recognition. There are three types of methods that are currently popular in the developed face recognition pattern, those are Eigen faces algorithm, Fisher faces algorithm and CNN neural network for face recognition
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Borkar, Yasar, Reeve Mascarenhas, Shubham Tambadkar, and Jayanand P. Gawande. "Comparison of Real-Time Face Detection and Recognition Algorithms." ITM Web of Conferences 44 (2022): 03046. http://dx.doi.org/10.1051/itmconf/20224403046.
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With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so on Face detection is a computer technology that determines the location and size of a person's face. It also creates a digital image of a human face. Face detection has been a standout topic in the science field This paper provides an in-depth examination of the various techniques investigated for face detection in digital images. Various face challenges and applications. This paper also discusses detection. Detection features are also provided. In addition, we hold special discussions on the practical aspects of developing a robust face detection system, and finally. This paper concludes with several promising research directions for the future.
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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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Poornima Raikar, Pranesh K, and Shreesha A Rao. "Attendance System for College Hostels Using Facial Recognition." International Journal of Current Research and Techniques 15, no. 1 (2025): 50380–84. https://doi.org/10.61359/2024050047.
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Face detection is a computer vision technology designed to identify and locate human faces in digital images. It is a specialized application of object detection, which involves identifying instances of specific semantic objects, such as humans, buildings, or vehicles, in images and videos. With advancements in technology, face detection has become increasingly significant in fields like photography, security, and marketing. This report presents an efficient approach to detecting and recognizing human faces using OpenCV and Python. It explores the pivotal role of machine learning in computer science and its application in facial detection through various OpenCV libraries. Furthermore, the report proposes a system for real-time human face detection, leveraging the integration of machine learning techniques with OpenCV and Python.
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Nikolaievskyi, O. Yu, O. V. Skliarenko, and A. I. Sidorchuk. "ANALYSIS AND COMPARISON OF FACE DETECTION APIS." Telecommunication and information technologies, no. 4 (2019): 39–45. http://dx.doi.org/10.31673/2412-4338.2019.043945.
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Mangmang, Geraldine B. "Face Mask Usage Detection Using Inception Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1660–67. http://dx.doi.org/10.5373/jardcs/v12sp7/20202272.
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Sukhinov, A. A., and G. B. Ostrobrod. "Efficient Face Detection on Epiphany Multicore Processor." COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES 1, no. 1 (2017): 113–27. http://dx.doi.org/10.23947/2587-8999-2017-1-1-113-127.
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Ma, Chenxing. "Comparative Analysis of CNN Based Face Detection." International Journal of Scientific Engineering and Research 11, no. 4 (2023): 49–53. https://doi.org/10.70729/se23419073937.
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Oualla, Mohamed, Khalid Ounachad, and Abdelalim Sadiq. "Building Face Detection with Face Divine Proportions." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 04 (2021): 63. http://dx.doi.org/10.3991/ijoe.v17i04.19149.
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<p class="0abstract"><span lang="EN-US">In this paper, we proposed an algorithm for detecting multiple human faces in an image based on haar-like features to represent the invariant characteristics of a face. The choice of relevant and more representative features is based on the divine proportions of a face. This technique, widely used in the world of beauty, especially in aesthetic medicine, allows the face to be divided into a set of specific regions according to known mathematical measures. Then we used the Adaboost algorithm for the learning phase. All of our work is based on the Viola and Jones algorithm, in particular their innovative technique called Integral Image, which calculates the value of a Haar-Like feature extracted from a face image. In the rest of this article, we will show that our approach is promising and can achieve high detection rates of up to 99%.</span></p>
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Kadhum, Aseil Nahum, and Aseel Nahum Kadhum. "Identifying People Wearing Masks in the Wild by Yolov7 Algorithm." International Academic Journal of Science and Engineering 11, no. 1 (2024): 229–36. http://dx.doi.org/10.9756/iajse/v11i1/iajse1126.
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Rapid face detection is an important matter at the present time, as face detection while wearing a mask has today become an important security matter in institutions, as well as protecting society from the spread of diseases, especially after the outbreak of infection with the Corona virus. Covid-19 virus. Due to the rapid spread of the Covid-19 pandemic, there is a need for society to adhere to wearing a mask in all public institutions, to prevent the spread of this disease. Researchers worked to find solutions to recognize faces and distinguish a person's identity. There was a problem in detecting faces and recognizing them easily, as researchers found many solutions to detect faces. So far, detecting faces while wearing a mask has problems with accuracy. In this research, a model of the deep learning algorithm, YOLOv7, will be used. It is a YOLO model that is characterized by accuracy and speed compared to previous YOLO models, and compared to a deep learning algorithm that performs two-stage detection, such as the CNN algorithm. Here the YOLOv7 model of the YOLO algorithm is proposed for face detection and recognition with and without mask. It is a model in which the structure of the algorithm has been modified, as it is distinguished by its speed in detecting faces compared to its predecessors. also reviewed most of the experiments with algorithm YOLO (You Only Look Once) detection algorithms and CNN (Convolutional Neural Network), and noticed that YOLOv7 is a better model than previous YOLO models in detecting faces while wearing a mask in terms of speed and accuracy. Face detection and discrimination has become very important at the present time from a security standpoint in all public places and requires accuracy and speed in detection.
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SAMAL, ASHOK, and PRASANA A. IYENGAR. "HUMAN FACE DETECTION USING SILHOUETTES." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (1995): 845–67. http://dx.doi.org/10.1142/s0218001495000353.
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Face detection is integral to any automatic face recognition system. The goal of this research is to develop a system that performs the task of human face detection automatically in a scene. A system to correctly locate and identify human faces will find several applications, some examples are criminal identification and authentication in secure systems. This work presents a new approach based on principal component analysis. Face silhouettes instead of intensity images are used for this research. It results in reduction in both space and processing time. A set of basis face silhouettes are obtained using principal component analysis. These are then used with a Hough-like technique to detect faces. The results show that the approach is robust, accurate and reasonably fast.
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Tran, The Vinh, Thi Khanh Tien Nguyen, and Kim Thanh Tran. "A survey on deep learning based face detection." Applied Aspects of Information Technology 6, no. 2 (2023): 201–12. http://dx.doi.org/10.15276/aait.06.2023.15.
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The article has focused on surveying face detection models based on deep learning, specifically examining different one-stage models in order to determine how to choose the appropriate face detection model as well as propose a direction to enhance our face detection model to match the actual requirements of computer vision application systems related to the face. The face detection models that were conducted survey include single shot detector, multi-task cascaded convolution neural networks, RetinaNet, YuNet on the Wider Face dataset. Tasks during the survey are structural investigation of chosen models, conducting experimental surveys to evaluate the accuracy and performance of these models. To evaluate and provide criteria for choosing face detection suitable for the requirements, two indicators are used, average precision to evaluate accuracy and frames-per-second to evaluate performance. Experiential results were analyzed and used for making conclusions and suggestions for future work. For our real-time applications on face-related camera systems, such as driver monitoring system, supermarket security system (shoplifting warning, disorderly warning), attendance system, often require fast processing, but still ensures accuracy. The models currently applied in our system such as Yolos, Single Shot Detector, MobileNetv1 guarantee real-time processing, but most of these models have difficulty in detecting small faces in the frame and cases containing contexts, which are easily mistaken for a human face. Meanwhile, the RetinaNet_ResNet50 model brings the highest accuracy, especially to ensure the detection of small faces in the frame, but the processing time is larger. Therefore, through this survey, we propose an enhancement direction of the face detection model based on the RetinaNet structure with the goal of ensuring accuracy and reducing processing time.