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
For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the character...For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the characteristic of human skin color clustering in the color space, the skin color area in YC b C r color space is extracted and a large number of irrelevant backgrounds are excluded; then for remedying the deficiencies of Adaboost algorithm, the cost-sensitive function is introduced into the Adaboost algorithm; finally the skin color segmentation and cost-sensitive Adaboost algorithm are combined for the face detection. Experimental results show that the proposed detection method has a higher detection rate and detection speed, which can more adapt to the actual field environment.展开更多
A new kind of region pair grey difference classifier was proposed. The regions in pairs associated to form a feature were not necessarily directly-connected, but were selected dedicatedly to the grey transition betwee...A new kind of region pair grey difference classifier was proposed. The regions in pairs associated to form a feature were not necessarily directly-connected, but were selected dedicatedly to the grey transition between regions coinciding with the face pattern structure. Fifteen brighter and darker region pairs were chosen to form the region pair grey difference features with high discriminant capabilities. Instead of using both false acceptance rate and false rejection rate, the mutual information was used as a unified metric for evaluating the classifying performance. The parameters of specified positions, areas and grey difference bias for each single region pair feature were selected by an optimization processing aiming at maximizing the mutual information between the region pair feature and classifying distribution, respectively. An additional region-based feature depicting the correlation between global region grey intensity patterns was also proposed. Compared with the result of Viola-like approach using over 2 000 features, the proposed approach can achieve similar error rates with only 16 features and 1/6 implementation time on controlled illumination images.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
Security access control systems and automatic video surveillance systems are becoming increasingly important recently,and detecting human faces is one of the indispensable processes.In this paper,an approach is presen...Security access control systems and automatic video surveillance systems are becoming increasingly important recently,and detecting human faces is one of the indispensable processes.In this paper,an approach is presented to detect faces in video surveillance.Firstly,both the skin-color and motion components are applied to extract skin-like regions.The skin-color segmentation algorithm is based on the BPNN (back-error-propagation neural network) and the motion component is obtained with frame difference algorithm.Secondly,the image is clustered into separated face candidates by using the region growing technique.Finally,the face candidates are further verified by the rule-based algorithm.Experiment results demonstrate that both the accuracy and processing speed are very promising and the approach can be applied for the practical use.展开更多
The intelligent environment needs Human-Computer Interactive technology （HCI） and a projector projects screen on wall in the intelligent environments. We propose the front-face detection from four captured images re...The intelligent environment needs Human-Computer Interactive technology （HCI） and a projector projects screen on wall in the intelligent environments. We propose the front-face detection from four captured images related to the intelligent room for the deaf. Our proposal purpose is that a deaf user faces wall displaying everywhere. system gets the images from four cameras, and detects the user region from a silhouette image using a different method, detects and cuts a motion body region from a different image, and cuts the vertexchest region from the cut body region image. The system attempts to find front-face using Haar-like feature, and selects a detected front-face image from the vertex-chest region. We estimate the front-face detection of recognition rate, which shows somewhat successfully.展开更多
The spread of social media has increased contacts of members of communities on the lntemet. Members of these communities often use account names instead of real names. When they meet in the real world, they will find ...The spread of social media has increased contacts of members of communities on the lntemet. Members of these communities often use account names instead of real names. When they meet in the real world, they will find it useful to have a tool that enables them to associate the faces in fiont of them with the account names they know. This paper proposes a method that enables a person to identify the account name of the person （＂target＂） in front of him/her using a smartphone. The attendees to a meeting exchange their identifiers （i.e., the account name） and GPS information using smartphones. When the user points his/her smartphone towards a target, the target＇s identifier is displayed near the target＇s head on the camera screen using AR （augmented reality）. The position where the identifier is displayed is calculated from the differences in longitude and latitude between the user and the target and the azimuth direction of the target from the user. The target is identified based on this information, the face detection coordinates, and the distance between the two. The proposed method has been implemented using Android terminals, and identification accuracy has been examined through experiments.展开更多
One being developed automatic sweep robot, need to estimate if anyone is on a certain range of road ahead then automatically adjust running speed, in order to ensure work efficiency and operation safety. This paper pr...One being developed automatic sweep robot, need to estimate if anyone is on a certain range of road ahead then automatically adjust running speed, in order to ensure work efficiency and operation safety. This paper proposed a method using face detection to predict the data of image sensor. The experimental results show that, the proposed algorithm is practical and reliable, and good outcome have been achieved in the application of instruction robot.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
Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view face detection and pose estimation is presented. A simple-to-...Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view face detection and pose estimation is presented. A simple-to-complex and coarse-to-fine view-based detector architecture has been designed to detect multi- view faces and estimate their poses efficiently. Both the pose estimators and the view-based face/nonface detectors are trained by a cost-sensitive AdaBoost algorithm to improve the generalization ability. Experi- mental results show that the proposed multi-view face detector, which can be constructed easily, gives more robust face detection and pose estimation and has a faster real-time detection speed compared with other conventional methods.展开更多
Although important progresses have been already made in face detection,many false faces can be found in detection results and false detection rate is influenced by some factors,such as rotation and tilt of human face,...Although important progresses have been already made in face detection,many false faces can be found in detection results and false detection rate is influenced by some factors,such as rotation and tilt of human face,complicated background,illumination,scale,cloak and hairstyle.This paper proposes a new method called DP-Adaboost algorithm to detect multi-angle human face and improve the correct detection rate.An improved Adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle face.An improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved Adaboost algorithm.Experiment results show that compared with the classical Adaboost algorithm with a frontal face classifier,the textual DP-Adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.展开更多
In this paper, we present a strategy to implement multi-pose face detection in compressed domain. The strategy extracts firstly feature vectors from DCT domain, and then uses a boosting algorithm to build classificrs ...In this paper, we present a strategy to implement multi-pose face detection in compressed domain. The strategy extracts firstly feature vectors from DCT domain, and then uses a boosting algorithm to build classificrs to distinguish faces and non-faces. Moreover, to get more accurate results of the face detection, we present a kernel function and a linear combination to build incrementally the strong classifiers based on the weak classifiers. Through comparing and analyzing results of some experiments on the synthetic data and the natural data, we can get more satisfied results by the strong classifiers than by the weak classifies. Key words weak classifier - boosting algorithm - face detection - compressed domain CLC number TP 391. 41 Foundation item: Supported by the National 863 Program (2002 AA11101) and Open Fund of State Technology Center of Multimedia Software Engineering (621-273128)Biography: CHEN Lei(1978-), male, Master, research direction: image process, image recognition and AI.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
The automatic detection of faces is a very important problem. The effectiveness of biometric authentication based on face mainly depends on the method used to locate the face in the image. This paper presents a hybrid...The automatic detection of faces is a very important problem. The effectiveness of biometric authentication based on face mainly depends on the method used to locate the face in the image. This paper presents a hybrid system for faces detection in unconstrained cases in which the illumination, pose, occlusion, and size of the face are uncontrolled. To do this, the new method of detection proposed in this paper is based primarily on a technique of automatic learning by using the decision of three neural networks, a technique of energy compaction by using the discrete cosine transform, and a technique of segmentation by the color of human skin. A whole of pictures （faces and no faces） are transformed to vectors of data which will be used for learning the neural networks to separate between the two classes. Discrete cosine transform is used to reduce the dimension of the vectors, to eliminate the redundancies of information, and to store only the useful information in a minimum number of coefficients while the segmentation is used to reduce the space of research in the image. The experimental results have shown that this hybridization of methods will give a very significant improvement of the rate of the recognition, quality of detection, and the time of execution.展开更多
This paper presents a new face detection approach to real-time applications, which is based on the skin color model and the morphological filtering. First the non-skin color pixels of the input image are removed based...This paper presents a new face detection approach to real-time applications, which is based on the skin color model and the morphological filtering. First the non-skin color pixels of the input image are removed based on the skin color model in the YC rC b chrominance space, from which we extract candidate human face regions. Then a mathematical morphological filter is used to remove noisy regions and fill the holes in the candidate skin color regions. We adopt the similarity between the human face features and the candidate face regions to locate the face regions in the original image. We have implemented the algorithm in our smart media system. The experiment results show that this system is effective in real-time applications.展开更多
To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machine...To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.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
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supported by the National Basic Research Program of China(973 Program)under Grant No.2012CB215202the National Natural Science Foundation of China under Grant No.51205046
文摘For face detection under complex background and illumination, a detection method that combines the skin color segmentation and cost-sensitive Adaboost algorithm is proposed in this paper. First, by using the characteristic of human skin color clustering in the color space, the skin color area in YC b C r color space is extracted and a large number of irrelevant backgrounds are excluded; then for remedying the deficiencies of Adaboost algorithm, the cost-sensitive function is introduced into the Adaboost algorithm; finally the skin color segmentation and cost-sensitive Adaboost algorithm are combined for the face detection. Experimental results show that the proposed detection method has a higher detection rate and detection speed, which can more adapt to the actual field environment.
基金Supported by the Joint Research Funds of Dalian University of Technology and Shenyang Automation Institute,Chinese Academy of Sciences
文摘A new kind of region pair grey difference classifier was proposed. The regions in pairs associated to form a feature were not necessarily directly-connected, but were selected dedicatedly to the grey transition between regions coinciding with the face pattern structure. Fifteen brighter and darker region pairs were chosen to form the region pair grey difference features with high discriminant capabilities. Instead of using both false acceptance rate and false rejection rate, the mutual information was used as a unified metric for evaluating the classifying performance. The parameters of specified positions, areas and grey difference bias for each single region pair feature were selected by an optimization processing aiming at maximizing the mutual information between the region pair feature and classifying distribution, respectively. An additional region-based feature depicting the correlation between global region grey intensity patterns was also proposed. Compared with the result of Viola-like approach using over 2 000 features, the proposed approach can achieve similar error rates with only 16 features and 1/6 implementation time on controlled illumination images.
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.
基金This work is supported by the National Natural Science
文摘Security access control systems and automatic video surveillance systems are becoming increasingly important recently,and detecting human faces is one of the indispensable processes.In this paper,an approach is presented to detect faces in video surveillance.Firstly,both the skin-color and motion components are applied to extract skin-like regions.The skin-color segmentation algorithm is based on the BPNN (back-error-propagation neural network) and the motion component is obtained with frame difference algorithm.Secondly,the image is clustered into separated face candidates by using the region growing technique.Finally,the face candidates are further verified by the rule-based algorithm.Experiment results demonstrate that both the accuracy and processing speed are very promising and the approach can be applied for the practical use.
基金supported by the Ministry of Knowledge Economy,Korea,the ITRC(Information Technology Research Center)support program(NIA-2009-(C1090-0902-0007))the Contents Technology Research Center support program
文摘The intelligent environment needs Human-Computer Interactive technology （HCI） and a projector projects screen on wall in the intelligent environments. We propose the front-face detection from four captured images related to the intelligent room for the deaf. Our proposal purpose is that a deaf user faces wall displaying everywhere. system gets the images from four cameras, and detects the user region from a silhouette image using a different method, detects and cuts a motion body region from a different image, and cuts the vertexchest region from the cut body region image. The system attempts to find front-face using Haar-like feature, and selects a detected front-face image from the vertex-chest region. We estimate the front-face detection of recognition rate, which shows somewhat successfully.
文摘The spread of social media has increased contacts of members of communities on the lntemet. Members of these communities often use account names instead of real names. When they meet in the real world, they will find it useful to have a tool that enables them to associate the faces in fiont of them with the account names they know. This paper proposes a method that enables a person to identify the account name of the person （＂target＂） in front of him/her using a smartphone. The attendees to a meeting exchange their identifiers （i.e., the account name） and GPS information using smartphones. When the user points his/her smartphone towards a target, the target＇s identifier is displayed near the target＇s head on the camera screen using AR （augmented reality）. The position where the identifier is displayed is calculated from the differences in longitude and latitude between the user and the target and the azimuth direction of the target from the user. The target is identified based on this information, the face detection coordinates, and the distance between the two. The proposed method has been implemented using Android terminals, and identification accuracy has been examined through experiments.
文摘One being developed automatic sweep robot, need to estimate if anyone is on a certain range of road ahead then automatically adjust running speed, in order to ensure work efficiency and operation safety. This paper proposed a method using face detection to predict the data of image sensor. The experimental results show that, the proposed algorithm is practical and reliable, and good outcome have been achieved in the application of instruction robot.
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.
文摘Locating multi-view faces in images with a complex background remains a challenging problem. In this paper, an integrated method for real-time multi-view face detection and pose estimation is presented. A simple-to-complex and coarse-to-fine view-based detector architecture has been designed to detect multi- view faces and estimate their poses efficiently. Both the pose estimators and the view-based face/nonface detectors are trained by a cost-sensitive AdaBoost algorithm to improve the generalization ability. Experi- mental results show that the proposed multi-view face detector, which can be constructed easily, gives more robust face detection and pose estimation and has a faster real-time detection speed compared with other conventional methods.
文摘Although important progresses have been already made in face detection,many false faces can be found in detection results and false detection rate is influenced by some factors,such as rotation and tilt of human face,complicated background,illumination,scale,cloak and hairstyle.This paper proposes a new method called DP-Adaboost algorithm to detect multi-angle human face and improve the correct detection rate.An improved Adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle face.An improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved Adaboost algorithm.Experiment results show that compared with the classical Adaboost algorithm with a frontal face classifier,the textual DP-Adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.
文摘In this paper, we present a strategy to implement multi-pose face detection in compressed domain. The strategy extracts firstly feature vectors from DCT domain, and then uses a boosting algorithm to build classificrs to distinguish faces and non-faces. Moreover, to get more accurate results of the face detection, we present a kernel function and a linear combination to build incrementally the strong classifiers based on the weak classifiers. Through comparing and analyzing results of some experiments on the synthetic data and the natural data, we can get more satisfied results by the strong classifiers than by the weak classifies. Key words weak classifier - boosting algorithm - face detection - compressed domain CLC number TP 391. 41 Foundation item: Supported by the National 863 Program (2002 AA11101) and Open Fund of State Technology Center of Multimedia Software Engineering (621-273128)Biography: CHEN Lei(1978-), male, Master, research direction: image process, image recognition and AI.
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supported by the Laboratory of Inverses Problems, Modeling, Information and Systems (PI:MIS), Department of Electronic and Telecommunication, University of 08 Mai 1945, Guelma, Algériathe Laboratory of Computer Research (LRI), Department of Computer Sciences, University of Badji Mokhtar, Annaba, Algéria
文摘The automatic detection of faces is a very important problem. The effectiveness of biometric authentication based on face mainly depends on the method used to locate the face in the image. This paper presents a hybrid system for faces detection in unconstrained cases in which the illumination, pose, occlusion, and size of the face are uncontrolled. To do this, the new method of detection proposed in this paper is based primarily on a technique of automatic learning by using the decision of three neural networks, a technique of energy compaction by using the discrete cosine transform, and a technique of segmentation by the color of human skin. A whole of pictures （faces and no faces） are transformed to vectors of data which will be used for learning the neural networks to separate between the two classes. Discrete cosine transform is used to reduce the dimension of the vectors, to eliminate the redundancies of information, and to store only the useful information in a minimum number of coefficients while the segmentation is used to reduce the space of research in the image. The experimental results have shown that this hybridization of methods will give a very significant improvement of the rate of the recognition, quality of detection, and the time of execution.
文摘This paper presents a new face detection approach to real-time applications, which is based on the skin color model and the morphological filtering. First the non-skin color pixels of the input image are removed based on the skin color model in the YC rC b chrominance space, from which we extract candidate human face regions. Then a mathematical morphological filter is used to remove noisy regions and fill the holes in the candidate skin color regions. We adopt the similarity between the human face features and the candidate face regions to locate the face regions in the original image. We have implemented the algorithm in our smart media system. The experiment results show that this system is effective in real-time applications.
基金National Natural Science Foundation of China(No.519705449)。
文摘To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.
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.
