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Journal articles on the topic 'Face detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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 h

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2

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 perfo

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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

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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 reve

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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 ro

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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 ro

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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

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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

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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 cond

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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 paramet

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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

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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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Khatke, Shreyash, Shubham Pachpute, Rajat Shingate, and Prof kajal Khalate. "Face Spoofing Detection Using Deep Learning." International Journal of Research Publication and Reviews 6, no. 6 (2025): 2475–81. https://doi.org/10.55248/gengpi.6.0625.2051.

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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 rea

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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 a

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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 recognit

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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 detecti

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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 rese

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Wakchaure, Shraddha, Avanti Tambe, Pratik Gadhave, Shubham Sandanshiv, and Mrs Archana Kadam. "Smart Exam Proctoring System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4507–10. http://dx.doi.org/10.22214/ijraset.2023.51358.

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Abstract: As the world is shifting towards digitalization, mostof the exams and assessments are being conducted online. These exams must be proctored. Several students are accessing thetest at the same time. It is very difficult to manually look if a student is committing malpractice. This project aims to use face detection and recognition for proctoring exams. Face detectionis the process of detecting faces in a video or image while face recognition is identifying or verifying a face from images orvideos. There are several research studies done on the detectionand recognition of faces owing t

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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 detect

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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 st

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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

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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 s

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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

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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

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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 com

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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 p

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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 t

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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

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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 indiv

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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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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

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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 o

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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 cla

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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 s

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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

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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 det

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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 ob

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