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Journal articles on the topic 'Face detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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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 (July 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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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 (April 30, 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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Nam, Amir Nobahar Sadeghi. "Face Detection." Volume 5 - 2020, Issue 9 - September 5, no. 9 (September 29, 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 (August 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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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 (June 30, 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 (June 2, 2016): 01–06. http://dx.doi.org/10.9756/sijcnce/v4i3/0103540102.

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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 (January 20, 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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Hashim, Siti, and Paul Mccullagh. "Face detection by using Haar Cascade Classifier." Wasit Journal of Computer and Mathematics Science 2, no. 1 (March 31, 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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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 (April 30, 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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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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Razzaq, Ali Nadhim, Rozaida Ghazali, Nidhal Khdhair El Abbadi, and Mohammad Dosh. "A Comprehensive Survey on Face Detection Techniques." Webology 19, no. 1 (January 20, 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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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 (July 25, 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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Bhange, Prof Anup. "Face Detection System with Face Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 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 (March 18, 2010): 839. http://dx.doi.org/10.1167/3.9.839.

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18

Robertson, David J., Rob Jenkins, and A. Mike Burton. "Face detection dissociates from face identification." Visual Cognition 25, no. 7-8 (June 2, 2017): 740–48. http://dx.doi.org/10.1080/13506285.2017.1327465.

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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 (May 31, 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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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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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 (January 2, 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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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 (April 6, 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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SAMAL, ASHOK, and PRASANA A. IYENGAR. "HUMAN FACE DETECTION USING SILHOUETTES." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (December 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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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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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 (June 6, 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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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 (July 3, 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.
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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 (August 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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Kawade, Amol. "Face Mask Detection using Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 817–20. http://dx.doi.org/10.22214/ijraset.2022.39926.

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Abstract: With the rise of pandemic the face mask became one of the most essential part of human life. This project aims to ease the maintenance of the rules and regulations by the authorities. This system will do the task of detecting the percentage mask worn on a person’s face. This way it will detect if a person has worn the mask or if he/she has worn it properly such that it would prevent the infections. If the mask is not worn then the person would be highlighted and post the detection, we can raise an alert if used in public places Keywords: Face mask, Python, MobileNetV2, Detection, highlighted.
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Baek, Yeong-Tae, and Seung-Bo Park. "Shot Type Detecting System using Face Detection." Journal of the Korea Society of Computer and Information 17, no. 9 (September 30, 2012): 49–56. http://dx.doi.org/10.9708/jksci/2012.17.9.049.

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Mishra, Vivek Kumar, Ravindra Gupta, and Ashvini Chaturvedi. "Face Profiler for Face Detection and Recognition." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 1117–20. http://dx.doi.org/10.26438/ijcse/v7i5.11171120.

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Balamurali, K., S. Chandru, Muhammed Sohail Razvi, and V. Sathiesh Kumar. "Face Spoof Detection Using VGG-Face Architecture." Journal of Physics: Conference Series 1917, no. 1 (June 1, 2021): 012010. http://dx.doi.org/10.1088/1742-6596/1917/1/012010.

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Singh, Avinash Kumar, Piyush Joshi, and G. C. Nandi. "Face liveness detection through face structure analysis." International Journal of Applied Pattern Recognition 1, no. 4 (2014): 338. http://dx.doi.org/10.1504/ijapr.2014.068327.

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Wu, Jianxin, and Zhi-Hua Zhou. "Efficient face candidates selector for face detection." Pattern Recognition 36, no. 5 (May 2003): 1175–86. http://dx.doi.org/10.1016/s0031-3203(02)00165-6.

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Thompson, Laura A., Daniel M. Malloy, John M. Cone, and David L. Hendrickson. "The face-to-face light detection paradigm." Interaction Studies 11, no. 2 (June 30, 2010): 336–48. http://dx.doi.org/10.1075/is.11.2.22tho.

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We introduce a novel paradigm for studying the cognitive processes used by listeners within interactive settings. This paradigm places the talker and the listener in the same physical space, creating opportunities for investigations of attention and comprehension processes taking place during interactive discourse situations. An experiment was conducted to compare results from previous research using videotaped stimuli to those obtained within the live face-to-face task paradigm. A headworn apparatus is used to briefly display LEDs on the talker’s face in four locations as the talker communicates with the participant. In addition to the primary task of comprehending speeches, participants make a secondary task light detection response. In the present experiment, the talker gave non-emotionally-expressive speeches that were used in past research with videotaped stimuli. Signal detection analysis was employed to determine which areas of the face received the greatest focus of attention. Results replicate previous findings using videotaped methods.
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Won, Bo-Whan, and Ja-Young Koo. "Rotated Face Detection Using Symmetry Detection." Journal of the Korea Society of Computer and Information 16, no. 1 (January 31, 2011): 53–59. http://dx.doi.org/10.9708/jksci.2011.16.1.053.

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Mohammed, Raidah Salim. "Neuro Fuzzy Network and Wavelet Gabor For Face Detection." Journal of Kufa for Mathematics and Computer 1, no. 8 (December 30, 2013): 48–57. http://dx.doi.org/10.31642/jokmc/2018/010807.

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This paper presents a face detection technique based on two techniques: wavelet Gabor filter for extract features from the localized facial image and neuro fuzzy system used as classifier depending on the features that extract , where it is used to determine the faces in the input image by draw boxes around the faces. The neurofuzzy network will be train on 128 image (69 face and 59 non face, size of each image 16*27 pixel in gray scale , this mean it trained to choose between two classes “face” and “non-face” images.   Our approach has been tested on eight common images with different face number in image and different number of fuzzy set. We got the best detection rate is 89.3% in case threshold equal 0.2 and in case number of fuzzy set equal 2. The stages of this work are implemented in MATLAB 7.0 environment.
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PHAM-NGOC, PHUONG-TRINH, TAE-HO KIM, and KANG-HYUN JO. "ROBUST FACE DETECTION FOR MOVING PICTURES UNDER POSE, ROTATION, ILLUMINATION AND OCCLUSION CHANGES." International Journal of Information Acquisition 04, no. 04 (December 2007): 291–302. http://dx.doi.org/10.1142/s0219878907001368.

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Face detection has been a key step in face analysis systems for decades. However, it is still a challenging task due to the variation in image background, view, pose, occlusion, etc. This paper proposes a simple and effective tool to detect human faces in moving pictures under such conditions. An improved approach aiming to reduce impacts of illumination, scale and connection of faces to receive rapidly skin homogeneous regions considered as the most potential face candidates is presented. A hybrid classifier, applied in retrieved face candidates, is based on template matching and appearance-based method providing a robust face detection. This verification achieves advantages of the powerful discrimination of Local Binary Patterns (LBPs) and the high speed detection capability of embedded Hidden Markov Models (eHMMs). Experiments were performed with different image databases and video sequences such as NRC-IIT facial video database, Caltech database, etc. Our system is effective in detecting not only frontal faces but also profile, rotated, occluded and connected ones for real-time application.
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Usha.T.R, Usha T. R., and Virupakshappa J.Nyamati. "“Face Detection Using Skin Color Segmentation In Images”." International Journal of Scientific Research 3, no. 2 (June 1, 2012): 146–47. http://dx.doi.org/10.15373/22778179/feb2014/47.

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Wang, Yan, and Zheng Wu Jiang. "Face Detection Based on Skin and Improved ICA." Advanced Materials Research 219-220 (March 2011): 1486–90. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1486.

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Face detection has been developed into one independent research direction. This paper proposes a detection method based on the combination of skin color and improved ICA. The method makes face detection in color images real and abandons the conventional way of detecting grey rather than color images. Its description is as follows: In the beginning, skin color mode should be founded on the basis of a new color space called H_SI_I. Then with the help of prior knowledge, image preprocessing can be implemented to obtain candidates’ faces, which will be detected by improved ICA. Experiments demonstrate that the method proposed by this paper has better effect than the conventional face detection based on subspace as far as speed and accuracy is concerned.
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Salman, Aditya, Mardhiya Hayaty, and Ika Nur Fajri. "Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method." JUITA : Jurnal Informatika 10, no. 2 (November 14, 2022): 217. http://dx.doi.org/10.30595/juita.v10i2.13223.

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In face recognition research, detecting several parts of the face becomes a necessary part of the study. The main factor in this work is lighting; some obstacles emerge when the low light's intensity falls in the process of face detection because of some conditions, such as weather, season, and sunlight. This study focuses on detecting faces in dim lighting using the Local Binary Pattern Histogram (LBPH) algorithm assisted by the Classifier Method, which is often used in face detection, namely the Haar Cascade Classifier. Furthermore, It will employ the image enhancement method, namely Histogram Equalization (HE), to improve the image source from the webcam. In the evaluation, different light intensities and various head poses affect the accuracy of the method. As a result, The research reaches 88% accuracy for successful face detection. Some factors such as head accessories, hair covering the face, and several parts of the face, like the eye, mouth, and nose that are invisible, should not be extreme.
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Omer, Yael, Roni Sapir, Yarin Hatuka, and Galit Yovel. "What Is a Face? Critical Features for Face Detection." Perception 48, no. 5 (April 2, 2019): 437–46. http://dx.doi.org/10.1177/0301006619838734.

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Faces convey very rich information that is critical for intact social interaction. To extract this information efficiently, faces should be easily detected from a complex visual scene. Here, we asked which features are critical for face detection. To answer this question, we presented non-face objects that generate a strong percept of a face (i.e., Pareidolia). One group of participants rated the faceness of this set of inanimate images. A second group rated the presence of a set of 12 local and global facial features. Regression analysis revealed that only the eyes or mouth significantly contributed to faceness scores. We further showed that removing eyes or mouth, but not teeth or ears, significantly reduced faceness scores. These findings show that face detection depends on specific facial features, the eyes and the mouth. This minimal information leads to over-generalization that generates false face percepts but assures that real faces are not missed.
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Tureckova, Alzbeta, Tomas Holik, and Zuzana Kominkova Oplatkova. "Dog Face Detection Using YOLO Network." MENDEL 26, no. 2 (December 21, 2020): 17–22. http://dx.doi.org/10.13164/mendel.2020.2.017.

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This work presents the real-world application of the object detection which belongs to one of the current research lines in computer vision. Researchers are commonly focused on human face detection. Compared to that, the current paper presents a challenging task of detecting a dog face instead that is an object with extensive variability in appearance. The system utilises YOLO network, a deep convolution neural network, to~predict bounding boxes and class confidences simultaneously. This paper documents the extensive dataset of dog faces gathered from two different sources and the training procedure of the detector. The proposed system was designed for realization on mobile hardware. This Doggie Smile application helps to snapshot dogs at the moment when they face the camera. The proposed mobile application can simultaneously evaluate the gaze directions of three dogs in scene more than 13 times per second, measured on iPhone XR. The average precision of the dogface detection system is 0.92.
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BEBIS, GEORGE, SATISHKUMAR UTHIRAM, and MICHAEL GEORGIOPOULOS. "FACE DETECTION AND VERIFICATION USING GENETIC SEARCH." International Journal on Artificial Intelligence Tools 09, no. 02 (June 2000): 225–46. http://dx.doi.org/10.1142/s0218213000000161.

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We consider the problem of searching for the face of a particular individual in a two-dimensional intensity image. This problem has many potential applications such as locating a person in a crowd using images obtained by surveillance cameras. There are two steps in solving this problem: first, face regions must be extracted from the image(s) (face detection) and second, candidate faces must be compared against a face of interest (face verification). Without any a-priori knowledge about the location and size of a face in an image, every possible image location and face size should be considered, leading to a very large search space. In this paper, we propose using Genetic Algorithms (GAs) for searching the image efficiently. Specifically, we use GAs to find image sub-windows that contain faces and in particular, the face of interest. Each sub-window is evaluated using a fitness function containing two terms: the first term favors sub-windows containing faces while the second term favors sub-windows containing faces similar to the face of interest. Both terms have been derived using the theory of eigenspaces. A set of increasingly complex scenes demonstrate the performance of the proposed genetic-search approach.
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P.Dahake, R., and M. U. Kharat. "Face Detection and Processing: a Survey." International Journal of Engineering & Technology 7, no. 4.19 (November 27, 2018): 1066. http://dx.doi.org/10.14419/ijet.v7i4.19.28287.

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In the recent era facial image processing is gaining more importance and the face detection from image or from video have number of applications which are video surveillance, entertainment, security, multimedia, communication, Ubiquitous computing etc. Various research work are carried out for face detection and processing which includes detection, tracking of the face, estimation of pose, clustering the detected faces etc. Although significant advances have been made, the performance of face detection systems provide satisfactory under controlled environment & may get degraded with some challenging scenario such as in real time video face detection and processing. There are many real-time applications where human face serves as identity and these application are time bound so time for detection of face from image or video and the further processing is very essential, thus here our goal is to discuss the face detection system overview and to review various human skin colors based approaches and Haar feature based approach for better detection performance. Detected faces tagging and clustering is essential in some cases, so for such further processing time factor plays important role. Some of the recent approaches to improve detection speed such as using Graphical Processing Unit are discussed and providing future directions in this area.
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Gedik, Onur, and Ayşe Demirhan. "Comparison of the Effectiveness of Deep Learning Methods for Face Mask Detection." Traitement du Signal 38, no. 4 (August 31, 2021): 947–53. http://dx.doi.org/10.18280/ts.380404.

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The usage of mask is necessary for the prevention and control of COVID-19 which is a respiratory disease that passes from person to person by contact and droplets from the respiratory tract. It is an important task to identify people who do not wear face mask in the community. In this study, performance comparison of the automated deep learning based models including the ones that use transfer learning for face mask detection on images was performed. Before training deep models, faces were detected within images using multi-task cascaded convolutional network (MTCNN). Images obtained from face mask detection dataset, COVID face mask detection dataset, mask detection dataset, and with/without mask dataset were used for training and testing the models. Face areas that are detected with MTCNN were used as input for convolutional neural network (CNN), MobileNetV2, VGG16 and ResNet50. VGG16 showed best performance with 97.82% accuracy. MobileNetV2 showed the worst performance for detecting faces without mask with 72.44% accuracy. Comparison results show that VGG16 can be used effectively to detect faces without mask. This system can be used in crowded public areas to warn people without mask that may help the reduce the risk of pandemic.
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46

Little, Anthony C., and Benedict C. Jones. "Attraction independent of detection suggests special mechanisms for symmetry preferences in human face perception." Proceedings of the Royal Society B: Biological Sciences 273, no. 1605 (September 19, 2006): 3093–99. http://dx.doi.org/10.1098/rspb.2006.3679.

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Symmetrical human faces are attractive and it has been proposed that humans have a specialized mechanism for detecting symmetry in faces and that sensitivity to symmetry determines symmetry preferences. Here, we show that symmetry preferences are influenced by inversion, whereas symmetry detection is not and that within individuals the ability to detect facial symmetry is not related to preferences for facial symmetry. Taken together, these findings suggest that symmetry preferences are indeed driven by a mechanism that is independent of conscious detection. A specialized mechanism for symmetry preference independent of detection may be the result of specific pressures faced by human ancestors to select high-quality mates and could support a modular view of mate choice. Unconscious mechanisms determining face preferences may explain why the reasons behind attraction are often difficult to articulate and demonstrate that detection alone cannot explain symmetry preferences.
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Gao, Jingqian, Minqiang Xu, Huan Wang, and Ji Zhou. "End-to-end Saliency Face Detection and Recognition." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012004. http://dx.doi.org/10.1088/1742-6596/2171/1/012004.

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Abstract Face recognition is a long-lasting hot topic in compute vision. The face recognition system mainly includes face detection, alignment and feature extraction. In the forward task, the extracted features are used to measure the similarity between faces, and outputs whether those are same person or not or which person it is in the registered set. Typically, the three stages of recognition system training independently of each other have the following shortcomings: 1) redundant calculation of feature maps; 2) unable to end-to-end optimization; 3) detecting an extracting so much useless face. A lightweight model for saliency face detection and recognition that can be optimized end-to-end is proposed. While maintaining accuracy, it meets the real-time and memory limitation requirements in embedded devices or terminals.
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48

Yang, Li, Min, and Wang. "Real-Time Pre-Identification and Cascaded Detection for Tiny Faces." Applied Sciences 9, no. 20 (October 15, 2019): 4344. http://dx.doi.org/10.3390/app9204344.

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Although the face detection problem has been studied for decades, searching tiny faces in the whole image is still a challenging task, especially in low-resolution images. Traditional face detection methods are based on hand-crafted features, but the features of tiny faces are different from those of normal-sized faces, and thus the detection robustness cannot be guaranteed. In order to alleviate the problem in existing methods, we propose a pre-identification mechanism and a cascaded detector (PMCD) for tiny-face detection. This pre-identification mechanism can greatly reduce background and other irrelevant information. The cascade detector is designed with two stages of deep convolutional neural network (CNN) to detect tiny faces in a coarse-to-fine manner, i.e., the face-area candidates are pre-identified as region of interest (RoI) based on a real-time pedestrian detector and the pre-identification mechanism, the set of RoI candidates is the input of the second sub-network instead of the whole image. Benefiting from the above mechanism, the second sub-network is designed as a shallow network which can keep high accuracy and real-time performance. The accuracy of PMCD is at least 4% higher than the other state-of-the-art methods on detecting tiny faces, while keeping real-time performance.
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49

Manjula, V. S. "ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 3 (April 30, 2013): 788–96. http://dx.doi.org/10.24297/ijct.v4i3.4201.

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In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification  which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting.   Thus the proposed technique   is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.  It is very useful in identify a single person from a group of faces.   Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier.  Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.
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Bhuvaneshwari, T., N. Ramadevi, and E. Kalpana. "Face Quality Detection in a Video Frame." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 2206–11. http://dx.doi.org/10.22214/ijraset.2023.55559.

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Abstract: Face detection technology is often used for surveillance of detecting and tracking of people in real time. The applications using these algorithms deal with low quality video feeds having less Pixels Per Inch (ppi) and/or low frame rate. The algorithms perform well with such video feeds, but their performance deteriorates towards high quality, high data-per-frame videos. This project focuses on developing such an algorithm that gives faster results on high quality videos, at par with the algorithms working on low quality videos. The proposed algorithm uses MTCNN as base algorithm, and speeds it up for highdefinition videos. This project also presents a novel solution to the problem of occlusion and detecting faces in videos. This survey provides an overview of the face detection from video literature, which predominantly focuses on visible wavelength face video as input. For the high-quality videos, we will Face-MTCNN and KLT, for low quality videos we will use MTCNN and KLT. Open issues and challenges are pointed out, i.e., highlighting the importance of comparability for algorithm evaluations and the challenge for future work to create Deep Learning (DL) approaches that are interpretable in addition to Track the faces. The suggested methodology is contrasted with conventional facial feature extraction for every frame and with well-known clustering techniques for a collection of videos
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