Journal articles: 'Multiple face detection'

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

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Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%

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Xiong, Yuyang, Wei Meng, Junwei Yan, and Jun Yang. "A Rotation-Invariance Face Detector Based on RetinaNet." Journal of Physics: Conference Series 2562, no. 1 (2023): 012066. http://dx.doi.org/10.1088/1742-6596/2562/1/012066.

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Abstract The use of deep convolutional neural networks has greatly improved the performance of general face detection. For detecting rotated faces, the mainstream approach is to use multi-stage detectors to gradually adjust the rotated face to a vertical orientation for detection, which increases the complexity of training as multiple networks are involved. In this study, we propose a new method for rotation-invariant face detection, which abandons the previously used cascaded architecture with multiple stages and instead uses a single-stage detector to achieve end-to-end detection of face classification, face box regression, and facial landmark regression. Extensive experiments on FDDB in multiple orientations have shown the effectiveness of our method. The results demonstrate that our method achieves good detection performance and the detection accuracy of our method even exceeds that of other rotated face detectors on the front-facing FDDB dataset.

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Chandrashekar, T. R., K. B. ShivaKumar, A. Srinidhi G, and A. K. Goutam. "PCA Based Rapid and Real Time Face Recognition Technique." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 12 (2013): 385–90. https://doi.org/10.5281/zenodo.14613535.

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Economical and efficient that is used in various applications is face Biometric which has been a popular form biometric system. Face recognition system is being a topic of research for last few decades. Several techniques are proposed to improve the performance of face recognition system. Accuracy is tested against intensity, distance from camera, and pose variance. Multiple face recognition is another subtopic which is under research now a day. Speed at which the technique works is a parameter under consideration to evaluate a technique. As an example a support vector machine performs really well for face recognition but the computational efficiency degrades significantly with increase in number of classes. Eigen Face technique produces quality features for face recognition but the accuracy is proved to be comparatively less to many other techniques. With increase in use of core processors in personal computers and application demanding speed in processing and multiple face detection and recognition system (for example an entry detection system in shopping mall or an industry), demand for such systems are cumulative as there is a need for automated systems worldwide. In this paper we propose a novel system of face recognition developed with C# .Net that can detect multiple faces and can recognize the faces parallel by utilizing the system resources and the core processors. The system is built around Haar Cascade based face detection and PCA based face recognition system with C#.Net. Parallel library designed for .Net is used to aide to high speed detection and recognition of the real time faces. Analysis of the performance of the proposed technique with some of the conventional techniques reveals that the proposed technique is not only accurate, but also is fast in comparison to other techniques. 

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

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

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Mohammed, W. Al-Neama, A. Mohamad Alshiha Abeer, and Ghanem Saeed Mustafa. "A parallel algorithm of multiple face detection on multi-core system." A parallel algorithm of multiple face detection on multi-core system 29, no. 2 (2023): 1166–73. https://doi.org/10.11591/ijeecs.v29.i2.pp1166-1173.

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This work offers a graphics processing unit (GPU)-based system for real-time face recognition, which can detect and identify faces with high accuracy. This work created and implemented novel parallel strategies for image integral, computation scan window processing, and classifier amplification and correction as part of the face identification phase of the Viola-Jones cascade classifier. Also, the algorithm and parallelized a portion of the testing step during the facial recognition stage were experimented with. The suggested approach significantly improves existing facial recognition methods by enhancing the performance of two crucial components. The experimental findings show that the proposed method, when implemented on an NVidia GTX 570 graphics card, outperforms the typical CPU program by a factor of 19.72 in the detection phase and 1573 in the recognition phase, with only 2000 images trained and 40 images tested. The recognition rate will plateau when the hardware's capabilities are maxed out. This demonstrates that the suggested method works well in real-time.

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Sun, Ke, Hong Liu, Qixiang Ye, et al. "Domain General Face Forgery Detection by Learning to Weight." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 2638–46. http://dx.doi.org/10.1609/aaai.v35i3.16367.

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In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that guarantees face detection performance across multiple domains, particularly the target domains that are never seen before. However, various face forgery methods cause complex and biased data distributions, making it challenging to detect fake faces in unseen domains. We argue that different faces contribute differently to a detection model trained on multiple domains, making the model likely to fit domain-specific biases. As such, we propose the LTW approach based on the meta-weight learning algorithm, which configures different weights for face images from different domains. The LTW network can balance the model's generalizability across multiple domains. Then, the meta-optimization calibrates the source domain's gradient enabling more discriminative features to be learned. The detection ability of the network is further improved by introducing an intra-class compact loss. Extensive experiments on several commonly used deepfake datasets to demonstrate the effectiveness of our method in detecting synthetic faces. Code and supplemental material are available at https://github.com/skJack/LTW.

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

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Abstract: As the world is shifting towards digitalization, mostof the exams and assessments are being conducted online. These exams must be proctored. Several students are accessing thetest at the same time. It is very difficult to manually look if a student is committing malpractice. This project aims to use face detection and recognition for proctoring exams. Face detectionis the process of detecting faces in a video or image while face recognition is identifying or verifying a face from images orvideos. There are several research studies done on the detectionand recognition of faces owing to the requirement for securityfor economic transactions, authorization, national safety andsecurity, and other important factors. Exam proctoring platformsshould be capable of detecting cheating and malpractices like face is not on the screen, gaze estimation, mobile phone detection,multiple face detection, etc. This project uses face identificationusing HAAR Cascades Algorithm and face recognition using theLocal Binary Pattern Histogram algorithm. This system can beused in the future in corporate offices, schools, and universities.

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Ms., Manisha Loharkar, Prof. Shital Wagh Ass, and Swati Bhavsar Dr. "MULTIPLE FACE DETECTION SYSTEM USING DEEP LEARNING." Journal of the Maharaja Sayajirao University of Baroda 59, no. 1 (I) (2025): 352–66. https://doi.org/10.5281/zenodo.15237844.

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

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Pailus, Rayner, and Rayner Alfred. "Performance Evaluation of MadBoost on Face Detection." Applied Mechanics and Materials 892 (June 2019): 200–209. http://dx.doi.org/10.4028/www.scientific.net/amm.892.200.

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Adaboost Viola-Jones method is indeed a profound discovery in detecting face images mainly because it is fast, light and one of the easiest methods of detecting face images among other techniques of face detection. Viola Jones uses Haar wavelet filter to detect face images and it produces almost 80%accuracy of face detection. This paper discusses proposed methodology and algorithms that involved larger library of filters used to create more discrimination features among the images by processing the proposed 15 Haar rectangular features (an extension from 4 Haar wavelet filters of Viola Jones) and used them in multiple adaptive ensemble process of detecting face image. After facial detection, the process continues with normalization processes by applying feature extraction such as PCA combined with LDA or LPP to extract our week learners’ wavelet for more classification features. Upon the process of feature extraction proposed feature selection to index these extracted data. These extracted vectors are used for training and creating MADBoost (Multiple Adaptive Diversified Boost)(an improvement of Adaboost, which uses multiple feature extraction methods combined with multiple classifiers) is able to capture, recognize and distinguish face image (s) faster. MADBoost applies the ensemble approach with better weights for classification to produce better face recognition results. Three experiments have been conducted to investigate the performance of the proposed MADBoost with three other classifiers, Neural Network (NN), Support Vector Machines (SVM) and Adaboost classifiers using Principal Component Analysis (PCA) as the feature extraction method. These experiments were tested against obstacles of POIES (Pose, Obstruction, Illumination, Expression, Sizes). Based on the results obtained, Madboost is found to be able to improve the recognition performance in matching failures, incorrect matching, matching success percentages and acceptable time taken to perform the classification task.

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Cheng, Xiao Ge, Amir As'ari Muhammad, and Anis Jasmin Sufri Nur. "Multiple face mask wearer detection based on YOLOv3 approach." International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 384–93. https://doi.org/10.11591/ijai.v12.i1.pp384-393.

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

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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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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%.

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Yun, Young-Ji, and Sung-Il Chien. "Face Detection Algorithm using Kinect-based Skin Color and Depth Information for Multiple Faces Detection." Journal of the Korea Contents Association 17, no. 1 (2017): 137–44. http://dx.doi.org/10.5392/jkca.2017.17.01.137.

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Zhang, Ming Hui, and Yao Yu Zhang. "The Adaboost Algorithm Applied in ATM Automatic Identification System." Advanced Materials Research 753-755 (August 2013): 2941–44. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2941.

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Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

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Chyad, Haitham Salman, Raniah Ali Mustafa, and Zainab Yasser Mohamed. "Edge Detection for Face Image Using Multiple Filters." International Journal of Engineering Research and Advanced Technology 07, no. 08 (2021): 28–41. http://dx.doi.org/10.31695/ijerat.2021.3736.

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Guo, Qi, Zhihui Wang, Daoerji Fan, and Huijuan Wu. "Multi-face detection and alignment using multiple kernels." Applied Soft Computing 122 (June 2022): 108808. http://dx.doi.org/10.1016/j.asoc.2022.108808.

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Amjad, A., A. Griffiths, and M. N. Patwary. "Multiple face detection algorithm using colour skin modelling." IET Image Processing 6, no. 8 (2012): 1093–101. http://dx.doi.org/10.1049/iet-ipr.2012.0167.

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Ge, Cheng Xiao, Muhammad Amir As’ari, and Nur Anis Jasmin Sufri. "Multiple face mask wearer detection based on YOLOv3 approach." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 384. http://dx.doi.org/10.11591/ijai.v12.i1.pp384-393.

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<div align="left"><a name="_Hlk108683337"></a>The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. </div>

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Savitha, A. C., Kumar KM Madhu, M. Pallavi, Chincholi Pallavi, H. B. Prethi, and Rachitha. "Experimental Detection of Deep Fake Images Using Face Swap Algorithm." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 56–61. https://doi.org/10.5281/zenodo.15397033.

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Deepfakes enable highly realistic face-swapping in videos using deep learning. To address the threat posed by Deepfakes, the DFDC dataset, the largest face-swapped video dataset to date, was created with over 100,000 clips generated using multiple methods, including Deepfake Autoencoders and GANs. The dataset consists of videos from 3,426 consenting actors. It supports the development of scalable Deepfake detection models and includes a public Kaggle competition to benchmark solutions. The dataset highlights the complexity of Deepfake detection but shows the potential for generalization to real-world scenarios. Deepfake creation using Generative Adversarial Networks (GANs) has grown rapidly, producing highly realistic fake images. This paper introduces a new detection method focused on analyzing the convolutional traces left by the GAN generation process. Using an Expectation Maximization (EM) algorithm, the approach extracts local features that reveal forensic traces in images. Validation was performed against five GAN architectures (GDWCT, STARGAN, ATTGAN, STYLEGAN, STYLEGAN2) using the CELEBA dataset. The results demonstrate the method's effectiveness in detecting Deepfakes and its potential for forensic investigations by identifying the generation process. We choose the Fake-or-Real dataset, which is the most recent benchmark dataset. The dataset was created with a text-to-speech model and is divided into four sub-datasets: for-rece, for-2-sec, for-norm and for-original. These datasets are classified into sub-datasets mentioned above according to audio length and bit rate. The experimental results show that the support vector machine (SVM) outperformed the other machine learning (ML) models in terms of accuracy on for-rece and for-2-sec datasets, while the gradient boosting model performed very well using for-norm dataset. The VGG-16 model produced highly encouraging results when applied to the for-original dataset. The VGG-16 model outperforms other state-of-the-art approaches. Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a super realistic form, in which it is difficult to distinguish between real and fake features.  

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Li, Hong-An, Jiangwen Fan, Jing Zhang, et al. "Facial Image Segmentation Based on Gabor Filter." Mathematical Problems in Engineering 2021 (February 9, 2021): 1–7. http://dx.doi.org/10.1155/2021/6620742.

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As an important part of face recognition, facial image segmentation has become a focus of human feature detection. In this paper, the AdaBoost algorithm and the Gabor texture analysis algorithm are used to segment an image containing multiple faces, which effectively reduces the false detection rate of facial image segmentation. In facial image segmentation, the image containing face information is first analyzed for texture using the Gabor algorithm, and appropriate thresholds are set with different thresholds of skin-like areas, where skin-like areas in the image’s background information are removed. Then, the AdaBoost algorithm is used to detect face regions, and finally, the detected face regions are segmented. Experiments show that this method can quickly and accurately segment faces in an image and effectively reduce the rate of missed and false detections.

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Zhang, Ming Hui, and Yao Yu Zhang. "Face Detection Methods Applied in ATM Automatic Identification System." Applied Mechanics and Materials 347-350 (August 2013): 3416–18. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3416.

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Seeing that there are some unsafe factors in the process of ATM using,an ATM automatic identification system with extend functions was developed. As human face features are unique, face detection is added to the ATM as a method of authentication. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

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Kulkarni, Narayan, and Ashok V. Sutagundar. "Detection of Human Facial Parts Using Viola-Jones Algorithm in Group of Faces." International Journal of Applied Evolutionary Computation 10, no. 1 (2019): 39–48. http://dx.doi.org/10.4018/ijaec.2019010103.

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Face detection is an image processing technique used in computer system to detect face in digital image. This article proposes an approach to detect faces and facial parts from an image of a group of people using the Viola Jones algorithm. Face detection is used in face recognition and identification systems. Automatic face detection and recognition is most challenging and a fast-growing research area in real-time applications like CC TV surveillance, video tracking, facial expression recognition, gesture recognition, human computer interaction, computer vision, and gender recognition. For face detection purposes various techniques and methods are applied in a computer system. In proposed system, a Viola Jones algorithm is implemented for multiple faces and facial parts and detected with a high rate of accuracy.

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Dhumal, Kalyani, Shravani Choudhary, Gayatri Alhat, Sanika Dhoke, and Vilas Rathod. "Automated Attendance System Using Multiple Facial Recognition." Journal of Scientific Advances 02, no. 01 (2025): 38–50. https://doi.org/10.63665/jsa.v2i1.01.

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The MITTrack app is a sophisticated face recognition-based intelligent attendance system that has been developed to mechanize the conventional manual attendance process in academic institutions. It integrates contemporary deep learning methods with mobile-friendly deployment to develop a fast, real-time solution. The system implements YOLOv8 for rapid and precise face detection from classroom pictures, allowing it to process several faces at a time. After detecting faces, MobileFaceNet (executed through TensorFlow Lite) creates light facial embeddings on the Android device. These facial embeddings are transmitted to a Flask-based backend server, where pre-registered student information is compared with DeepFace to authenticate identities. On successful matching, attendance is marked and securely stored in a local SQLite database. MITTrack is optimized for usage in offline or local networks, with accessibility without relying on the internet. With over 95% accuracy, the system has provided a secure, contactless, and scalable means of tracking attendance that makes it well-apt for usage in classrooms, training centres, and business environments.

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Shastri, Dhiresh. "Automated Attendance System Using Multiple Facial Recognition." Journal Of Scientific Advances 2, no. 1 (2025): 38–50. https://doi.org/10.63665/f26pe709.

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The MITTrack app is a sophisticated face recognition-based intelligent attendance system that has been developed to mechanize the conventional manual attendance process in academic institutions. It integrates contemporary deep learning methods with mobile-friendly deployment to develop a fast, real-time solution. The system implements YOLOv8 for rapid and precise face detection from classroom pictures, allowing it to process several faces at a time. After detecting faces, MobileFaceNet (executed through TensorFlow Lite) creates light facial embeddings on the Android device. These facial embeddings are transmitted to a Flask-based backend server, where pre-registered student information is compared with DeepFace to authenticate identities. On successful matching, attendance is marked and securely stored in a local SQLite database. MITTrack is optimized for usage in offline or local networks, with accessibility without relying on the internet. With over 95% accuracy, the system has provided a secure, contactless, and scalable means of tracking attendance that makes it well-apt for usage in classrooms, training centres, and business environments.

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Ouyang, Junlin, Jiayong Ma, and Beijing Chen. "GAN-Generated Face Detection Based on Multiple Attention Mechanism and Relational Embedding." Information Technology and Control 53, no. 2 (2024): 408–28. http://dx.doi.org/10.5755/j01.itc.53.2.35590.

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The rapid development of the Generative Adversarial Network (GAN) makes generated face images more and more visually indistinguishable, and the detection performance of previous methods will degrade seriously when the testing samples are out-of-sample datasets or have been post-processed. To address the above problems, we propose a new relational embedding network based on “what to observe” and “where to attend” from a relational perspective for the task of generated face detection. In addition, we designed two attention modules to effectively utilize global and local features. Specifically, the dual-self attention module selectively enhances the representation of local features through both image space and channel dimensions. The cross-correlation attention module computes similarity between images to capture the global information of the output in the image. We conducted extensive experiments to validate our method, and the proposed algorithm can effectively extract the correlations between features and achieve satisfactory generalization and robustness in generating face detection. In addition, we also explored the design of the model structure and the inspection performance on more categories of generated images (not limited to faces). The results show that RENet also has good detection performance on datasets other than faces.

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Lai, Zhimao, Yang Guo, Yongjian Hu, Wenkang Su, and Renhai Feng. "Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach." Sensors 24, no. 24 (2024): 8075. https://doi.org/10.3390/s24248075.

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Makeup modifies facial textures and colors, impacting the precision of face anti-spoofing systems. Many individuals opt for light makeup in their daily lives, which generally does not hinder face identity recognition. However, current research in face anti-spoofing often neglects the influence of light makeup on facial feature recognition, notably the absence of publicly accessible datasets featuring light makeup faces. If these instances are incorrectly flagged as fraudulent by face anti-spoofing systems, it could lead to user inconvenience. In response, we develop a face anti-spoofing database that includes light makeup faces and establishes a criterion for determining light makeup to select appropriate data. Building on this foundation, we assess multiple established face anti-spoofing algorithms using the newly created database. Our findings reveal that the majority of these algorithms experience a decrease in performance when faced with light makeup faces. Consequently, this paper introduces a general face anti-spoofing algorithm specifically designed for light makeup faces, which includes a makeup augmentation module, a batch channel normalization module, a backbone network updated via the Exponential Moving Average (EMA) method, an asymmetric virtual triplet loss module, and a nearest neighbor supervised contrastive module. The experimental outcomes confirm that the proposed algorithm exhibits superior detection capabilities when handling light makeup faces.

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Satpathy, Ankita, Shubham Rajendra Shelke, Atharva Madan Shelavale, and Sheetal Ghongte. "IRIS RECOGNITION: BIOMETRIC AUTHENTICATION FOR COCKPIT DOOR SAFETY." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02404.

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A face recognition system is one of the biometric types of information process. Its applicability is easier, and the working range is larger than other fingerprint, passcode, and signature. The face recognition system also enhances and improves security and privacy to a great extent, leading towards a better and modernized future. The system uses a combination of two techniques: face detection and recognition. The face detection is performed on live acquired images without any application field in mind [1,2]. The processes utilized in the system are white balance correction, skin-like region segmentation, facial feature extraction, and face image extraction on a candidate. The system is also capable of detecting and recognizing multiple faces in live acquired images. A face recognition system makes the process easier and minimizes the issues related to security [3]. Keywords: Cockpit, Iris Recognition, Smart Door, Biometric Identification, Security

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Wang, Tao, Jia-Jun Bu, and Chun Chen. "A color based face detection system using multiple templates." Journal of Zhejiang University-SCIENCE A 4, no. 2 (2003): 162–65. http://dx.doi.org/10.1631/jzus.2003.0162.

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Loutas, E., I. Pitas, and C. Nikou. "Probabilistic Multiple Face Detection and Tracking Using Entropy Measures." IEEE Transactions on Circuits and Systems for Video Technology 14, no. 1 (2004): 128–35. http://dx.doi.org/10.1109/tcsvt.2003.819178.

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Choi, Jeahoon, Seong Joon Yoo, Sung Wook Baik, Ho Chul Shin, and Dongil Han. "Rotation Invariant Multiple Face-detection Architecture for Smart TV." IERI Procedia 6 (2014): 33–38. http://dx.doi.org/10.1016/j.ieri.2014.03.006.

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Kanotra, Rohan, Akash, Neelendu Wadhwa, and Dr N. Jeyanthi*. "Comparative Analysis of Object Detection Algorithms for Face Mask Detection." International Journal of Engineering and Advanced Technology 10, no. 4 (2021): 148–51. http://dx.doi.org/10.35940/ijeat.c2284.0410421.

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COVID-19 has made mankind see unprecedented and unbelievable times with millions of people being affected due to it. Multiple countries have started vaccinating their populations in the hope that it will end the pandemic. Given the inequitable access to vaccines across the world and the highly mutating coronavirus it remains to be seen when will everyone get access to vaccines and how effective the vaccines might prove over the virus variants. Therefore, standard COVID behaviour is here to stay for some time. Wearing face masks is one such etiquette which greatly reduces risk of getting infected. Employing public face mask detection systems has helped multiple countries to bring the pandemic under control. In this paper we have done a quantitative analysis of different object detection algorithms namely ResNet,MobileNetV2 and CNN on face mask detection on accuracy and recall parameters using an unbiased, large and diverse dataset in order the algorithm which can be applied on a mass scale.

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Rohan, Kanotra, Akash, Wadhwa Neelendu, and Jeyanthi N. "Comparative Analysis of Object Detection Algorithms for Face Mask Detection." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 4 (2021): 148–51. https://doi.org/10.35940/ijeat.C2284.0410421.

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COVID-19 has made mankind see unprecedented and unbelievable times with millions of people being affected due to it. Multiple countries have started vaccinating their populations in the hope that it will end the pandemic. Given the inequitable access to vaccines across the world and the highly mutating coronavirus it remains to be seen when will everyone get access to vaccines and how effective the vaccines might prove over the virus variants. Therefore, standard COVID behaviour is here to stay for some time. Wearing face masks is one such etiquette which greatly reduces risk of getting infected. Employing public face mask detection systems has helped multiple countries to bring the pandemic under control. In this paper we have done a quantitative analysis of different object detection algorithms namely ResNet,MobileNetV2 and CNN on face mask detection on accuracy and recall parameters using an unbiased, large and diverse dataset in order the algorithm which can be applied on a mass scale.

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Wang, Dong Mei, Ming Ma, and Yan Sun. "Sensitive Webpage Filter Based on Multiple Filtering." Applied Mechanics and Materials 241-244 (December 2012): 2891–96. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2891.

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In order to improve the accuracy and real-time performance of webpage filtering, a sensitive webpage filter based on multiple filtering was designed. Firstly, the URL is gained from IE browser’s address bar by BHO technology; Secondly, match the webpage text with sensitive vocabulary database using SMA algorithm; Finally, use the sensitive image detecting algorithm combing face detection, skin detection, skin text detection and classification to filter sensitive images in the webpage. The Simulation experimental results showed that the sensitive webpage filter can effectively intercept and filter sensitive webpages, meeting the accuracy and the real-time requirement of webpage filtering.

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Duan, Ding Bo, and Jian Ma. "Context-Assisted Fast Face Detection." Applied Mechanics and Materials 571-572 (June 2014): 863–66. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.863.

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In this paper, we propose a novel algorithm for fast face detection in camera video. By extending the task of face detection from solely relying on visual data of camera sensor to cooperatively analyzing synchronized information of multiple sensors, we greatly reduce the time cost of face detection. Specifically, when subjects wearing motion sensors move around in the field of view (FOV) of a camera, motion status estimated from the wearable sensors helps to decide when is the good time to start face detection, and thus save large amount of work previously spent on filtering out faceless frames. To test the technical feasibility and efficiency of the proposed method, we conducted extensive experiments and compared it with state-of-the-art algorithms. Results indicate that the proposed algorithm achieves significant improvements in terms of time cost.

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Mankar, Ishan. "Smart Surveillance System Using RESNET-50 and MTCNN." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 4380–86. http://dx.doi.org/10.22214/ijraset.2024.62554.

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Abstract: In recent years, advances in computer vision and artificial intelligence have led to the development of sophisticated surveillance systems capable of tracking and identifying individuals in a variety of situations. This research presents a new intelligent surveillance system designed to track multiple people in a video and generate a comprehensive log file to keep records of identified people. The proposed system integrates state-of-the-art techniques in face detection and recognition to achieve accurate and efficient identification of people in video streams. The system uses a custom dataset collected using a script, which captures images of individuals' faces in various conditions and environments, and fine-tunes a pre-trained ResNet50 model for face recognition tasks.In addition, face detection is performed using the MTCNN (Multi-Task Cascade Convolutional Neural Network) algorithm, which ensures robust face detection under various conditions. The intelligent tracking system works by analyzing each frame of the input video, detecting faces using the MTCNN algorithm, and then identifying individuals using a trained face recognition model. Identified individuals are logged with a time stamp, providing a comprehensive record of their presence in the surveillance area over time

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Ariff, F. N. M., H. Jaafar, S. N. H. Jusoh, and N. A. F. Haris. "Single and Multiface Detection and Recognition System." Journal of Physics: Conference Series 2312, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1742-6596/2312/1/012036.

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Abstract Face detection has drawn the interest of numerous research groups because to its vast application in various domains such as surveillance and security systems, as human-computer interaction, and many more. Face identification is the important phase involving several factors such as lighting, facial expression, and ageing effects. It’s more tough as detection takes a lot of time to detect and distinguish a single face at a time. Moreover, most of the existing technology cannot accurately detect many faces simultaneously. This study therefore presents a system that can recognize and identify multiple face image simultaneously with various expressions. Face-recognition procedure consists of data gathering, face detection, extraction, and classification feature. The face dataset is obtained from 10 participants with varied backgrounds and expressions. Subsequently, the viola-jones technique together with threshold technique is utilized in face detection to detect face presents while removing the unnecessary background to reduce face recognition time processing further. The Principal Component Analysis (PCA) is then employed to extract features while maintaining as much information as possible from enormous image data set. After formulating each face’s representation, the classification process is considered to recognize the identities of users’ faces. Here, a non-parametric classifier i.e. Support Vector Machine (SVM) is applied in this process. Conclusively, the system is able to detect around 90 percent multi-face user in different conditions.

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Vimal, Chamandeep. "Face Detection’s Various Techniques and Approaches: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 839–43. http://dx.doi.org/10.22214/ijraset.2022.39890.

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Abstract: In the past few years, face recognition owned significant consideration and is appreciated as one of the most promising applications in the field of image analysis. Verification and Identification have become a significant issue in the present computerized world. Various variabilities are present across human faces such as pose, expression, position and orientation, skin colour, the presence of glasses or facial hair, variations in camera gain, lighting conditions, and image resolution, because of these variabilities face detection is very complicated. In this paper, several existing face detection methods and strategies are analyzed and studied. The main goal of this paper is to present or suggest an approach that is an excellent choice for face detection. Keywords: Face detection, Recognition, CPU, Multiple layer

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Foun, Ming Him. "The Study of Performance for Face Detection Based on Multiple Representative Convolutional Neural Networks." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 45–51. http://dx.doi.org/10.54097/hset.v57i.9895.

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Due to the diversity of deep learning models, choosing the suitable model for a specific task can be rather onerous. In this paper, the performance of three deep convolutional neural networks, namely VGG16, ResNet50, and MobileNetV2 on face detection were compared. Each model was trained on a dataset of 11,900 images from the FDDB dataset that included various face sizes and orientations with multiple augmentations, including color alteration, blurring, and flipping. The final layers of the models were modified into a binary classification model and a regression model indicating face found and coordinates of the facial bounding box. The models were trained on the same basis of 40 epochs with batch size 64 with binary cross entropy loss and DIoU loss and a learning rate of 0.0001 with a learning rate decay of 0.8 per epoch. The experimental results demonstrated that VGG16 outperformed ResNet50 and MobileNetV2 in terms of accuracy, with VGG16 achieving the highest score of 0.9240, followed by ResNet50 with a score of 0.8568, and MobileNetV2 with an accuracy of 0.6028. The results suggest that VGG16 is a more suitable choice for face detection applications than ResNet50 and MobileNetV2, while ResNet50 and MobileNetV2 may provide higher accuracy for other image recognition tasks or real time face detections. The findings in this paper can contribute to the selection of appropriate deep learning models for face detection.

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Hua-Chun, Yang, and Xu An Wang. "A Study on Components and Features in Face Detection." International Journal of Information Technology and Web Engineering 10, no. 3 (2015): 33–45. http://dx.doi.org/10.4018/ijitwe.2015070103.

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It's natural and convenient that the face is used as a feature in the city safety monitoring. Components and feature extraction are two key problems in face detection. To address the local occlusion and pose variation in face detection, face can be looked on as a whole composed of several parts from up to down. First, the face is divided into a number of local regions from which various features are extracted. Each region is identified by a local classifier and is assigned a preliminary part label. A random field is established based on these labels and multiple dependencies between different parts are modeled in a CRF framework. The probability that the test image may be a face is calculated by a trained CRF model. The probability is used as a measure to test the existence of a face. The experiments were carried out on the CMU/MIT dataset. As indicated by the experiment results, the following methods can improve the detection rate and enhance the robustness of face detection in case of occlusion: 1) integrating multiple features and multiple dependencies in CRF framework; 2) dividing the face optimally.

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Yu, Bo, Ian Lane, and Fang Chen. "3D Face Detection via Reconstruction Over Hierarchical Features for Single Face Situations." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 04 (2016): 1655013. http://dx.doi.org/10.1142/s0218001416550132.

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There are multiple challenges in face detection, including illumination conditions and diverse poses of the user. Prior works tend to detect faces by segmentation at pixel level, which are generally not computationally efficient. When people are sitting in the car, which can be regarded as single face situations, most face detectors fail to detect faces under various poses and illumination conditions. In this paper, we propose a simple but efficient approach for single face detection. We train a deep learning model that reconstructs face directly from input image by removing background and synthesizing 3D data for only the face region. We apply the proposed model to two public 3D face datasets, and obtain significant improvements in false rejection rate (FRR) of 4.6% (from 4.6% to 0.0%) and 21.7% (from 30.2% to 8.5%), respectively, compared with state-of-art performances in two datasets. Furthermore, we show that our reconstruction approach can be applied using 1/2 the time of a widely used real-time face detector. These results demonstrate that the proposed Reconstruction ConNet (RN) is both more accurate and efficient for real-time face detection than prior works.

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MISS, KRASHNA V. PANPALIYA, and SAGAR S. BADNERKAR PROF. "A REVIEW ON: MULTIPLE FACE DETECTION AND TRACKING USING SKIN TONE SEGMENTATION TECHNIQUE." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 6 (2017): 172–77. https://doi.org/10.5281/zenodo.1445959.

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 Recent years have witnessed renewed interest in developing skin segmentation approaches. Skin feature segmentation has been widely employed in different aspects of computer vision applications including face detection and tracking systems. This is mostly due to the attractive characteristics of skin color and its effectiveness to object segmentation. On the contrary, there are certain challenges in using human skin color as a feature to segment dynamic face features, due to various illumination conditions, complicated environment, and computation time or real-time method. These challenges have led to the insufficiency of many of the skin color segmentation approaches. Therefore, to produce simple, effective, and cost efficient skin segmentation, a localized approach for multiple face detection and tracking in input video are proposed which is based on skin tone segmentation algorithm and dynamic facial features (eyes, nose, and mouth) extraction method which increase accuracy of detection and tracking multiple faces in video. In this proposed work, segmentation of skin regions from a video frames is done by using RGB color model which help to remove non skin like pixel from a video frames. Each segmented skin regions are tested to know whether region contain human face or not, by extracting facial features. Once the face is detected then they are tracked in video. The experimental results showed that the proposed scheme achieved good performance in terms of accuracy and computation time. https://journalnx.com

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Ulyanov, N. A., S. V. Yaskevich, Dergach P. A., and A. V. YablokovAV. "Detection of records of weak local earthquakes using neural networks." Russian Journal of Geophysical Technologies, no. 2 (January 13, 2022): 13–23. http://dx.doi.org/10.18303/2619-1563-2021-2-13.

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Manual processing of large volumes of continuous observations produced by local seismic networks takes a lot of time, therefore, to solve this problem, automatic algorithms for detecting seismic events are used. Deterministic methods for solving the problem of detection, which do an excellent job of detecting intensive earthquakes, face critical problems when detecting weak seismic events (earthquakes). They are based on principles based on the calculation of energy, which causes multiple errors in detection: weak seismic events may not be detected, and high-amplitude noise may be mistakenly detected as an event. In our work, we propose a detection method capable of surpassing deterministic methods in detecting events on seismograms, successfully detecting a similar or more events with fewer false detections.

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Zhang, Xinjun, Jia Liu, and Yu Sang. "Face and Background Deepfake Detection Algorithm." Journal of Physics: Conference Series 2632, no. 1 (2023): 012026. http://dx.doi.org/10.1088/1742-6596/2632/1/012026.

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Abstract The paper proposes the Face and Background Deepfake Detection (FBDD) algorithm to detect both face forgery and background forgery. Multiple key frames of the video are extracted as the input of the FBDD Transformer, and the inter-frame information is also considered to obtain four feature vectors of the person, background, head, and face to improve the detection efficiency and accuracy. FBDD distance is proposed to determine the type of video forgery. Constructing a rich dataset of video forgery methods containing face forgery and background forgery improves the accuracy of the model on different datasets. The FBDD algorithm was experimented on several commonly used and article-constructed datasets and has higher accuracy and stronger image degradation generalization compared to frontier detection algorithms.

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Tyagi, Ranbeer, Geetam Singh Tomar, and Laxmi Shrivastava. "Unconstrained Face Detection of Multiple Humans Present in the Video." Wireless Personal Communications 118, no. 2 (2021): 901–17. http://dx.doi.org/10.1007/s11277-020-08050-2.

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Singh, Manminder, and A. S. Arora. "A Novel Face Liveness Detection Algorithm with Multiple Liveness Indicators." Wireless Personal Communications 100, no. 4 (2018): 1677–87. http://dx.doi.org/10.1007/s11277-018-5661-1.

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Al-Neama, Mohammed Wajid, Abeer A. Mohamad Alshiha, and Mustafa Ghanem Saeed. "A parallel algorithm of multiple face detection on multi-core system." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (2023): 1166. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp1166-1173.

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This work offers a graphics processing unit (GPU)-based system for real-time face recognition, which can detect and identify faces with high accuracy. This work created and implemented novel parallel strategies for image integral, computation scan window processing, and classifier amplification and correction as part of the face identification phase of the Viola-Jones cascade classifier. Also, the algorithm and parallelized a portion of the testing step during the facial recognition stage were experimented with. The suggested approach significantly improves existing facial recognition methods by enhancing the performance of two crucial components. The experimental findings show that the proposed method, when implemented on an NVidia GTX 570 graphics card, outperforms the typical CPU program by a factor of 19.72 in the detection phase and 1573 in the recognition phase, with only 2000 images trained and 40 images tested. The recognition rate will plateau when the hardware's capabilities are maxed out. This demonstrates that the suggested method works well in real-time.

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Zhu, Zhishan, Aike Liu, and Haifei Chi. "Temperature measurement and identity detection system based on multiple embedded control units and LBP detection algorithm." E3S Web of Conferences 252 (2021): 01048. http://dx.doi.org/10.1051/e3sconf/202125201048.

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This paper designs a simple non-contact temperature measurement and identity recognition device based on multiple embedded control systems and feature recognition algorithms. The device can achieve multiple functions such as non-contact temperature measurement, face recognition, mask recognition, and smart alarm. The system consists of three parts: main control, interactive system and detection system: the main control selects STM32F407VGT6 to process the data returned by multiple sensors and realize the mutual communication of each system; the interactive system uses the HMI serial touch screen to realize the visualization of data and human Machine operation function; the detection system is equipped with MLX90614 temperature detection module and OpenMV4 machine vision module to realize functions such as temperature detection, face recognition and mask recognition[1]. In addition, in order to ensure the accuracy and stability of the detection results, this article specifically designs temperature data filtering and compensation algorithms, LBP feature detection algorithms and other intelligent algorithms. Through experiments, the accuracy of this device to detect 28°C-48°C is within 0.8°C, and the accuracy of identifying faces and masks is above 98%.

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Tang, Qijian, Yanfei Li, Yinhe Cai, Xiang Peng, and Xiaoli Liu. "Face Detection Based on DF-Net." Electronics 12, no. 19 (2023): 4021. http://dx.doi.org/10.3390/electronics12194021.

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Face data have found increasingly widespread applications in daily life. To efficiently and accurately extract face information from input images, this paper presents a DF-Net-based face detection approach. A lightweight facial feature extraction neural network based on the MobileNet-v2 architecture is designed and implemented. By incorporating multi-scale feature fusion and spatial pyramid modules, the system achieves face localization and extraction across multiple scales. The proposed network is trained on the open-source face detection dataset WiderFace. The hyperparameters such as bottleneck coefficients and quality factors are discussed. Comparative experiments with other commonly used networks are carried out in terms of network model size, processing speed, and network extraction accuracy. Experimental results affirm the efficacy and robustness of this method, especially in challenging facial poses.

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HIREMATH, P. S., and AJIT DANTI. "DETECTION OF MULTIPLE FACES IN AN IMAGE USING SKIN COLOR INFORMATION AND LINES-OF-SEPARABILITY FACE MODEL." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 01 (2006): 39–61. http://dx.doi.org/10.1142/s021800140600451x.

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In this paper, human faces are detected using the skin color information and the Lines-of-Separability (LS) face model. The various skin color spaces based on widely used color models such as RGB, HSV, YCbCr, YUV and YIQ are compared and an appropriate color model is selected for the purpose of skin color segmentation. The proposed approach of skin color segmentation is based on YCbCr color model and sigma control limits for variations in its color components. The segmentation by the proposed method is found to be more efficient in terms of speed and accuracy. Each of the skin segmented regions is then searched for the facial features using the LS face model to detect the face present in it. The LS face model is a geometric approach in which the spatial relationships among the facial features are determined for the purpose of face detection. Hence, the proposed approach based on the combination of skin color segmentation and LS face model is able to detect single as well as multiple faces present in a given image. The experimental results and comparative analysis demonstrate the effectiveness of this approach.

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Zhao, Yuanzhang, and Shengling Geng. "Face occlusion detection algorithm based on yolov5." Journal of Physics: Conference Series 2031, no. 1 (2021): 012053. http://dx.doi.org/10.1088/1742-6596/2031/1/012053.

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Abstract The current face-mask recognition detection algorithm during the epidemic only distinguishes between wearing or not wearing a mask. Such detection often has certain loopholes, such as using other objects to cover their mouths and noses instead of masks to cheat the detection. To address such problems, this paper proposes a YOLOv5 based face occlusion detection algorithm, which is modified based on the YOLOv5 algorithm by improving the loss function as DIoU and increasing the experimental samples by introducing multiple data sets to improve the object detection effect. The experimental results show that the improved YOLOv5 algorithm has improved the object detection effect for different kinds of face occlusions, which verifies the method’s effectiveness.

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

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

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Journal articles on the topic 'Multiple face detection'

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