Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments...Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.展开更多
Biometric applications widely use the face as a component for recognition and automatic detection.Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and ro...Biometric applications widely use the face as a component for recognition and automatic detection.Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation.This problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been devised.The objective of the paper is to implement a robust method for face detection taken at various angle.Further to achieve better results than known algorithms for face detection.In RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid manner.The MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation characteristics.In this manner,RIFDS can detect human faces at different rotations and with different facial expressions.The RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena images.The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.展开更多
Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. Th...Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.展开更多
Background Several face detection and recogni tion methods have been proposed in the past decades that have excellent performance.The conventional face recognition pipeline comprises the following:(1)face detection,(2...Background Several face detection and recogni tion methods have been proposed in the past decades that have excellent performance.The conventional face recognition pipeline comprises the following:(1)face detection,(2)face alignment,(3)feature extraction,and(4)similarity,which are independent of each other.The separate facial analysis stages lead to redundant model calculations,and are difficult for use in end-to-end training.Methods In this paper,we propose a novel end-to-end trainable convolutional network framework for face detection and recognition,in which a geometric transformation matrix is directly learned to align the faces rather than predicting the facial landmarks.In the training stage,our single CNN model is supervised only by face bounding boxes and personal identities,which are publicly available from WIDER FACE and CASIA-WebFace datasets.Our model is tested on Face Detection Dataset and Benchmark(FDDB)and Labeled Face in the Wild(LFW)datasets.Results The results show 89.24%recall for face detection tasks and 98.63%accura cy for face recognition tasks.展开更多
A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask...A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper. The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process. For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics. By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%.展开更多
Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it...Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.展开更多
Hajj and Umrah are two main religious duties for Muslims.To help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for...Hajj and Umrah are two main religious duties for Muslims.To help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each place.Since the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management process.In this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence techniques.Human detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security purposes.The proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and tracking.The main contribution of this work focuses on the detection and tracking model which is based on an improved object detection model.The improved Yolo v4 was used for face detection and tracking.It has been very effective in detecting small objects in highresolution images.The novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired task.Channel wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building blocks.The main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive proposals.We demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available dataset.The wider faces dataset was used for the train and the evaluation of the proposed detection and tracking model.The proposed model has achieved good results with 91.2%of mAP and a processing speed of 18 FPS on the Nvidia GTX 960 GPU.展开更多
Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance manag...Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.展开更多
In recent years,with the rapid growth of generative adversarial networks(GANs),a photo-realistic face can be easily generated from a random vector.Moreover,the faces generated by advanced GANs are very realistic.It is...In recent years,with the rapid growth of generative adversarial networks(GANs),a photo-realistic face can be easily generated from a random vector.Moreover,the faces generated by advanced GANs are very realistic.It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces.Therefore,detecting the face generated by GANs is a necessary work.This paper mainly introduces some methods to detect GAN-generated fake faces,and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes,and the results obtained in the respective data sets.On this basis,the challenges faced in this field and future research directions are discussed.展开更多
Due to the power of editing tools,new types of fake faces are being created and synthesized,which has attracted great attention on social media.It is reasonable to acknowledge that one human cannot distinguish whether...Due to the power of editing tools,new types of fake faces are being created and synthesized,which has attracted great attention on social media.It is reasonable to acknowledge that one human cannot distinguish whether the face is manipulated from the real faces.Therefore,the detection of face manipulation becomes a critical issue in digital media forensics.This paper provides an overview of recent deep learning detection models for face manipulation.Some public dataset used for face manipulation detection is introduced.On this basis,the challenges for the research and the potential future directions are analyzed and discussed.展开更多
Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns.Despite the considerable progress in existing methods,we note that:Previous works overlooked finegrain f...Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns.Despite the considerable progress in existing methods,we note that:Previous works overlooked finegrain forgery cues with high transferability.Such cues positively impact the model’s accuracy and generalizability.Moreover,single-modality often causes overfitting of the model,and Red-Green-Blue(RGB)modal-only is not conducive to extracting the more detailed forgery traces.We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues.First,we propose two functional modules to reveal and locate the deeper forged features.Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module,which can excavate the association between dualmodal space and channels and enhance the learning of subtle noise features.A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality.The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning.Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.展开更多
The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial ma...The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.展开更多
This research presents an improved real-time face recognition system at a low<span><span><span style="font-family:" color:red;"=""> </span></span></span><...This research presents an improved real-time face recognition system at a low<span><span><span style="font-family:" color:red;"=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px and 98.05% at 45</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px and 95% at 45</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px respectively. A facial deflection of about 30</span></span></span><span><span><span><span><span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#ffffff;"="">°</span></span><span> on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.</span></span></span></span>展开更多
The COVID-19 pandemic is a virus that has disastrous effects onhuman lives globally;still spreading like wildfire causing huge losses to humanityand economies. There is a need to follow few constraints like social dis...The COVID-19 pandemic is a virus that has disastrous effects onhuman lives globally;still spreading like wildfire causing huge losses to humanityand economies. There is a need to follow few constraints like social distancingnorms, personal hygiene, and masking up to effectively control the virus spread.The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtainedfor mask detection are found to be effective. The system is trained using4500 images to accurately judge and justify its accuracy. The aim is to developan algorithm to automatically detect a mask, but the approach does not facilitatethe percentage of improper usage. Accuracy levels are as low as 50% if the maskis improperly covered and an alert is raised for improper placement. It can be usedat traffic places and social gatherings for the prevention of virus transmission. Itworks by first locating the region of interest by creating a frame boundary, thenfacial points are picked up to detect and concentrate on specific features. Thetraining on the input images is performed using different epochs until the artificialface mask detection dataset is created. The system is implemented using TensorFlow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-sourcedatasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier isused to load and pre-process the image dataset for building a fully connectedhead. The objective is to assess the accuracy of the identification, measuringthe efficiency and effectiveness of algorithms for precision, recall, and F1 score.展开更多
In recent years,face detection has attracted much attention and achieved great progress due to its extensively practical applications in the field of face based computer vision.However,the tradeoff between accuracy an...In recent years,face detection has attracted much attention and achieved great progress due to its extensively practical applications in the field of face based computer vision.However,the tradeoff between accuracy and efficiency of the face detectors still needs to be further studied.In this paper,using Darknet-53 as backbone,we propose an improved YOLOv3-attention model by introducing attention mechanism and data augmentation to obtain the robust face detector with high accuracy and efficiency.The attention mechanism is introduced to enhance much higher discrimination of the deep features,and the trick of data augmentation is used in the training procedure to achieve higher detection accuracy without significantly affecting the inference speed.The model has been trained and evaluated on the popular and challenging face detection benchmark,i.e.,the WIDER FACE training and validation subsets,respectively,achieving AP of 0.942,0.919 and 0.821 with the speed of 28FPS.This performance exceeds some existing SOTA algorithms,demonstrating acceptable accuracy and near real time detection for VGA resolution images,even in the complex scenarios.In addition,the proposed model shows good generation ability on another public dataset FDDB.The results indicate the proposed model is a promising face detector with high efficiency and accuracy in the wild.展开更多
Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy,with the brim-full horizon yet to unfold.In the absence of effective antiviral a...Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy,with the brim-full horizon yet to unfold.In the absence of effective antiviral and limited medical resources,many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources.Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals.Regardless of discourse on medical resources and diversities in masks,all countries are mandating coverings over nose and mouth in public areas.Towards contribution of public health,the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask.The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy.We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps.In addition,we also propose a bounding box transformation to improve localization performance during mask detection.The experiments are conducted with three popular baseline models namely ResNet50,AlexNet and MobileNet.We explored the possibility of these models to plug-in with the proposed model,so that highly accurate results can be achieved in less inference time.It is observed that the proposed technique can achieve high accuracy(98.2%)when implemented with ResNet50.Besides,the proposed model can generate 11.07%and 6.44%higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.展开更多
In recent years,the COVID-19 pandemic has negatively impacted all aspects of social life.Due to ease in the infected method,i.e.,through small liquid particles from the mouth or the nose when people cough,sneeze,speak...In recent years,the COVID-19 pandemic has negatively impacted all aspects of social life.Due to ease in the infected method,i.e.,through small liquid particles from the mouth or the nose when people cough,sneeze,speak,sing,or breathe,the virus can quickly spread and create severe problems for people’s health.According to some research as well as World Health Organization(WHO)recommendation,one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space.A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection.This simple method can reduce up to 95%of the spread of the particles.However,this solution depends heavily on social consciousness,which is sometimes unstable.In order to improve the effectiveness of wearing face masks in public spaces,this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask.The approach uses the deep learning technique that relies on GoogleNet,AlexNet,and VGG16 models.The results are synthesized by an ensemble method,i.e.,the bagging technique.From the experimental results,the approach represents a more than 95%accuracy of face mask recognition.展开更多
Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the enti...Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the entire environ-ment.Whenfinding a suspicious person,the tracked object cannot be locked in time for tracking.This research replaces the traditionalfixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article,we proposed a TIMT(The Intel-ligent Monitoring and Tracking)algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent(AI)face recognition technology and the OpenPose which is able to monitor the phy-sical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote con-trol interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore,the system couldfind abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community,and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone sys-tem has an excellent performance,which not only dramatically reduces the num-ber of monitors and the required equipment but also achieves perfect monitoring and tracking.展开更多
Class attendance is important.Class attendance recording is often done using“roll-call”or signing attendance registers.These are time consuming,easy to cheat,and it is difficult to draw any information from them.The...Class attendance is important.Class attendance recording is often done using“roll-call”or signing attendance registers.These are time consuming,easy to cheat,and it is difficult to draw any information from them.There are other,expensive alternatives to automate attendance recording with varying accuracy.This study experimented with a smartphone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully,while keeping the solution cost-effective.The effect of different class sizes was also investigated.The research was done within a pragmatism philosophy,using a prototype in a field experiment.The algorithms that were used are Viola–Jones(Haar features),deep neural network and histogram of oriented gradients for detection,and eigenfaces,fisherfaces,and local binary pattern histogram for recognition.The best combination was Viola–Jones combined with fisherfaces,with a mean accuracy of 54%for a class of 10 students and 34.5%for a class of 22 students.The best all over performance on a single class photo was 70%(class size 10).As is,this prototype is not accurate enough to use,but with a few adjustments,it may become a cheap,easy-to-implement solution to the attendance recording problem.展开更多
<p align="justify"> <span style="font-family:Verdana;">Amid the Covid-19 widespread, it has been challenging for educational institutions to conduct online classes, facing multiples cha...<p align="justify"> <span style="font-family:Verdana;">Amid the Covid-19 widespread, it has been challenging for educational institutions to conduct online classes, facing multiples challenges. This paper provides an insight into different approaches in facing those challenges which includes conducting a fair online class for students. It is tough for an instructor to keep track of their students at the same time because it is difficult to screen if any of the understudies within the class are not present, mindful, or drowsing. This paper discusses a possible solution, something new that can offer support to instructors seeing things from a more significant point of view. The solution is a facial analysis computer program that can let instructors know which students are attentive and who is not. There’s a green and red square box for face detection, for which Instructors can watch by seeing a green box on those mindful students conjointly, a red box on those who are not mindful at all. This paper finds that the program can automatically give attendance by analyzing data from face detection. It has other features for which the teacher can also know if any student leaves the class early. In this paper, model design, performance analysis, and online class assistant aspects of the program have been discussed.</span> </p>展开更多
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R349)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Face recognition technology automatically identifies an individual from image or video sources.The detection process can be done by attaining facial characteristics from the image of a subject face.Recent developments in deep learning(DL)and computer vision(CV)techniques enable the design of automated face recognition and tracking methods.This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking(HHODL-AFDT)method.The proposed HHODL-AFDT model involves a Faster region based convolution neural network(RCNN)-based face detection model and HHO-based hyperparameter opti-mization process.The presented optimal Faster RCNN model precisely rec-ognizes the face and is passed into the face-tracking model using a regression network(REGN).The face tracking using the REGN model uses the fea-tures from neighboring frames and foresees the location of the target face in succeeding frames.The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work.The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60%and 88.08%under PICS and VTB datasets,respectively.
基金The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project Number No-R-2021-154.
文摘Biometric applications widely use the face as a component for recognition and automatic detection.Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation.This problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been devised.The objective of the paper is to implement a robust method for face detection taken at various angle.Further to achieve better results than known algorithms for face detection.In RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid manner.The MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation characteristics.In this manner,RIFDS can detect human faces at different rotations and with different facial expressions.The RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena images.The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.
文摘Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.
文摘Background Several face detection and recogni tion methods have been proposed in the past decades that have excellent performance.The conventional face recognition pipeline comprises the following:(1)face detection,(2)face alignment,(3)feature extraction,and(4)similarity,which are independent of each other.The separate facial analysis stages lead to redundant model calculations,and are difficult for use in end-to-end training.Methods In this paper,we propose a novel end-to-end trainable convolutional network framework for face detection and recognition,in which a geometric transformation matrix is directly learned to align the faces rather than predicting the facial landmarks.In the training stage,our single CNN model is supervised only by face bounding boxes and personal identities,which are publicly available from WIDER FACE and CASIA-WebFace datasets.Our model is tested on Face Detection Dataset and Benchmark(FDDB)and Labeled Face in the Wild(LFW)datasets.Results The results show 89.24%recall for face detection tasks and 98.63%accura cy for face recognition tasks.
文摘A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper. The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process. For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics. By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R442),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.
基金This work was funded by the University of Jeddah,Jeddah,Saudi Arabia,under Grant No.(UJ-21-ICL-4)The authors,therefore,acknowledge with thanks the University of Jeddah technical and financial support.
文摘Hajj and Umrah are two main religious duties for Muslims.To help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each place.Since the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management process.In this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence techniques.Human detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security purposes.The proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and tracking.The main contribution of this work focuses on the detection and tracking model which is based on an improved object detection model.The improved Yolo v4 was used for face detection and tracking.It has been very effective in detecting small objects in highresolution images.The novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired task.Channel wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building blocks.The main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive proposals.We demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available dataset.The wider faces dataset was used for the train and the evaluation of the proposed detection and tracking model.The proposed model has achieved good results with 91.2%of mAP and a processing speed of 18 FPS on the Nvidia GTX 960 GPU.
基金financial supports from the National Natural Science Foundation of China (No. 51134024)the National High Technology Research and Development Program of China (No. 2012AA062203)are gratefully acknowledged
文摘Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.
基金supported by National Natural Science Foundation of China(62072251).
文摘In recent years,with the rapid growth of generative adversarial networks(GANs),a photo-realistic face can be easily generated from a random vector.Moreover,the faces generated by advanced GANs are very realistic.It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces.Therefore,detecting the face generated by GANs is a necessary work.This paper mainly introduces some methods to detect GAN-generated fake faces,and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes,and the results obtained in the respective data sets.On this basis,the challenges faced in this field and future research directions are discussed.
基金This work is supported by National Natural Science Foundation of China(62072251).
文摘Due to the power of editing tools,new types of fake faces are being created and synthesized,which has attracted great attention on social media.It is reasonable to acknowledge that one human cannot distinguish whether the face is manipulated from the real faces.Therefore,the detection of face manipulation becomes a critical issue in digital media forensics.This paper provides an overview of recent deep learning detection models for face manipulation.Some public dataset used for face manipulation detection is introduced.On this basis,the challenges for the research and the potential future directions are analyzed and discussed.
基金This study is supported by the Fundamental Research Funds for the Central Universities of PPSUC under Grant 2022JKF02009.
文摘Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns.Despite the considerable progress in existing methods,we note that:Previous works overlooked finegrain forgery cues with high transferability.Such cues positively impact the model’s accuracy and generalizability.Moreover,single-modality often causes overfitting of the model,and Red-Green-Blue(RGB)modal-only is not conducive to extracting the more detailed forgery traces.We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues.First,we propose two functional modules to reveal and locate the deeper forged features.Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module,which can excavate the association between dualmodal space and channels and enhance the learning of subtle noise features.A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality.The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning.Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.
文摘The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.
文摘This research presents an improved real-time face recognition system at a low<span><span><span style="font-family:" color:red;"=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px and 98.05% at 45</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px and 95% at 45</span></span></span><span><span><span style="font-family:;" "=""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">px respectively. A facial deflection of about 30</span></span></span><span><span><span><span><span style="color:#4F4F4F;font-family:-apple-system, " font-size:16px;white-space:normal;background-color:#ffffff;"="">°</span></span><span> on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.</span></span></span></span>
文摘The COVID-19 pandemic is a virus that has disastrous effects onhuman lives globally;still spreading like wildfire causing huge losses to humanityand economies. There is a need to follow few constraints like social distancingnorms, personal hygiene, and masking up to effectively control the virus spread.The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtainedfor mask detection are found to be effective. The system is trained using4500 images to accurately judge and justify its accuracy. The aim is to developan algorithm to automatically detect a mask, but the approach does not facilitatethe percentage of improper usage. Accuracy levels are as low as 50% if the maskis improperly covered and an alert is raised for improper placement. It can be usedat traffic places and social gatherings for the prevention of virus transmission. Itworks by first locating the region of interest by creating a frame boundary, thenfacial points are picked up to detect and concentrate on specific features. Thetraining on the input images is performed using different epochs until the artificialface mask detection dataset is created. The system is implemented using TensorFlow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-sourcedatasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier isused to load and pre-process the image dataset for building a fully connectedhead. The objective is to assess the accuracy of the identification, measuringthe efficiency and effectiveness of algorithms for precision, recall, and F1 score.
基金This work is partially supported by National Key Research and Development Project(Grant No.A19808)Fundamental Research Funds for the Central Universities(Grant No.2019JKF225).
文摘In recent years,face detection has attracted much attention and achieved great progress due to its extensively practical applications in the field of face based computer vision.However,the tradeoff between accuracy and efficiency of the face detectors still needs to be further studied.In this paper,using Darknet-53 as backbone,we propose an improved YOLOv3-attention model by introducing attention mechanism and data augmentation to obtain the robust face detector with high accuracy and efficiency.The attention mechanism is introduced to enhance much higher discrimination of the deep features,and the trick of data augmentation is used in the training procedure to achieve higher detection accuracy without significantly affecting the inference speed.The model has been trained and evaluated on the popular and challenging face detection benchmark,i.e.,the WIDER FACE training and validation subsets,respectively,achieving AP of 0.942,0.919 and 0.821 with the speed of 28FPS.This performance exceeds some existing SOTA algorithms,demonstrating acceptable accuracy and near real time detection for VGA resolution images,even in the complex scenarios.In addition,the proposed model shows good generation ability on another public dataset FDDB.The results indicate the proposed model is a promising face detector with high efficiency and accuracy in the wild.
文摘Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy,with the brim-full horizon yet to unfold.In the absence of effective antiviral and limited medical resources,many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources.Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals.Regardless of discourse on medical resources and diversities in masks,all countries are mandating coverings over nose and mouth in public areas.Towards contribution of public health,the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask.The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy.We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps.In addition,we also propose a bounding box transformation to improve localization performance during mask detection.The experiments are conducted with three popular baseline models namely ResNet50,AlexNet and MobileNet.We explored the possibility of these models to plug-in with the proposed model,so that highly accurate results can be achieved in less inference time.It is observed that the proposed technique can achieve high accuracy(98.2%)when implemented with ResNet50.Besides,the proposed model can generate 11.07%and 6.44%higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.
文摘In recent years,the COVID-19 pandemic has negatively impacted all aspects of social life.Due to ease in the infected method,i.e.,through small liquid particles from the mouth or the nose when people cough,sneeze,speak,sing,or breathe,the virus can quickly spread and create severe problems for people’s health.According to some research as well as World Health Organization(WHO)recommendation,one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space.A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection.This simple method can reduce up to 95%of the spread of the particles.However,this solution depends heavily on social consciousness,which is sometimes unstable.In order to improve the effectiveness of wearing face masks in public spaces,this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask.The approach uses the deep learning technique that relies on GoogleNet,AlexNet,and VGG16 models.The results are synthesized by an ensemble method,i.e.,the bagging technique.From the experimental results,the approach represents a more than 95%accuracy of face mask recognition.
文摘Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the entire environ-ment.Whenfinding a suspicious person,the tracked object cannot be locked in time for tracking.This research replaces the traditionalfixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article,we proposed a TIMT(The Intel-ligent Monitoring and Tracking)algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent(AI)face recognition technology and the OpenPose which is able to monitor the phy-sical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote con-trol interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore,the system couldfind abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community,and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone sys-tem has an excellent performance,which not only dramatically reduces the num-ber of monitors and the required equipment but also achieves perfect monitoring and tracking.
文摘Class attendance is important.Class attendance recording is often done using“roll-call”or signing attendance registers.These are time consuming,easy to cheat,and it is difficult to draw any information from them.There are other,expensive alternatives to automate attendance recording with varying accuracy.This study experimented with a smartphone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully,while keeping the solution cost-effective.The effect of different class sizes was also investigated.The research was done within a pragmatism philosophy,using a prototype in a field experiment.The algorithms that were used are Viola–Jones(Haar features),deep neural network and histogram of oriented gradients for detection,and eigenfaces,fisherfaces,and local binary pattern histogram for recognition.The best combination was Viola–Jones combined with fisherfaces,with a mean accuracy of 54%for a class of 10 students and 34.5%for a class of 22 students.The best all over performance on a single class photo was 70%(class size 10).As is,this prototype is not accurate enough to use,but with a few adjustments,it may become a cheap,easy-to-implement solution to the attendance recording problem.
文摘<p align="justify"> <span style="font-family:Verdana;">Amid the Covid-19 widespread, it has been challenging for educational institutions to conduct online classes, facing multiples challenges. This paper provides an insight into different approaches in facing those challenges which includes conducting a fair online class for students. It is tough for an instructor to keep track of their students at the same time because it is difficult to screen if any of the understudies within the class are not present, mindful, or drowsing. This paper discusses a possible solution, something new that can offer support to instructors seeing things from a more significant point of view. The solution is a facial analysis computer program that can let instructors know which students are attentive and who is not. There’s a green and red square box for face detection, for which Instructors can watch by seeing a green box on those mindful students conjointly, a red box on those who are not mindful at all. This paper finds that the program can automatically give attendance by analyzing data from face detection. It has other features for which the teacher can also know if any student leaves the class early. In this paper, model design, performance analysis, and online class assistant aspects of the program have been discussed.</span> </p>
