Face antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems.Face recognition is commonly used for authentication in surveillance applications.How...Face antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems.Face recognition is commonly used for authentication in surveillance applications.However,attackers try to compromise these systems by using spoofing techniques such as using photos or videos of users to gain access to services or information.Many existing methods for face spoofing face difficulties when dealing with new scenarios,especially when there are variations in background,lighting,and other environmental factors.Recent advancements in deep learning with multi-modality methods have shown their effectiveness in face antispoofing,surpassing single-modal methods.However,these approaches often generate several features that can lead to issues with data dimensionality.In this study,we introduce a multimodal deep fusion network for face anti-spoofing that incorporates cross-axial attention and deep reinforcement learning techniques.This network operates at three patch levels and analyzes images from modalities(RGB,IR,and depth).Initially,our design includes an axial attention network(XANet)model that extracts deeply hidden features from multimodal images.Further,we use a bidirectional fusion technique that pays attention to both directions to combine features from each mode effectively.We further improve feature optimization by using the Enhanced Pity Beetle Optimization(EPBO)algorithm,which selects the features to address data dimensionality problems.Moreover,our proposed model employs a hybrid federated reinforcement learning(FDDRL)approach to detect and classify face anti-spoofing,achieving a more optimal tradeoff between detection rates and false positive rates.We evaluated the proposed approach on publicly available datasets,including CASIA-SURF and GREATFASD-S,and realized 98.985%and 97.956%classification accuracy,respectively.In addition,the current method outperforms other state-of-the-art methods in terms of precision,recall,and Fmeasures.Overall,the developed methodology boosts the effectiveness of our model in detecting various types of spoofing attempts.展开更多
The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(...The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.展开更多
As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many de...As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many deep learning-based facial forgery detection approaches show promise,they often fail to delve deeply into the complex relationships between image features and forgery indicators,limiting their effectiveness to specific forgery techniques.To address this challenge,we propose a dual-branch collaborative deepfake detection network.The network processes video frame images as input,where a specialized noise extraction module initially extracts the noise feature maps.Subsequently,the original facial images and corresponding noise maps are directed into two parallel feature extraction branches to concurrently learn texture and noise forgery clues.An attention mechanism is employed between the two branches to facilitate mutual guidance and enhancement of texture and noise features across four different scales.This dual-modal feature integration enhances sensitivity to forgery artifacts and boosts generalization ability across various forgery techniques.Features from both branches are then effectively combined and processed through a multi-layer perception layer to distinguish between real and forged video.Experimental results on benchmark deepfake detection datasets demonstrate that our approach outperforms existing state-of-the-art methods in terms of detection performance,accuracy,and generalization ability.展开更多
With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communicati...With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communication,image is widely used as a carrier of communication because of its rich content,intuitive and other advantages.Image recognition based on convolution neural network is the first application in the field of image recognition.A series of algorithm operations such as image eigenvalue extraction,recognition and convolution are used to identify and analyze different images.The rapid development of artificial intelligence makes machine learning more and more important in its research field.Use algorithms to learn each piece of data and predict the outcome.This has become an important key to open the door of artificial intelligence.In machine vision,image recognition is the foundation,but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition.Predecessors have provided many model algorithms,which have laid a solid foundation for the development of artificial intelligence and image recognition.The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network.Different from full connection network,convolutional neural network does not use full connection method in each layer of neurons of neural network,but USES some nodes for connection.Although this method reduces the computation time,due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation,this paper improves the model to be a multi-level information fusion of the convolution calculation method,and further recovers the discarded feature information,so as to improve the recognition rate of the image.VGG divides the network into five groups(mimicking the five layers of AlexNet),yet it USES 3*3 filters and combines them as a convolution sequence.Network deeper DCNN,channel number is bigger.The recognition rate of the model was verified by 0RL Face Database,BioID Face Database and CASIA Face Image Database.展开更多
This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists ...This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists of a high-accuracy single stage detector(SSD)and an efficient tiny convolutional neural network(T-CNN)for joint face detection refinement,alignment and attribute analysis.Though the SSD face detectors achieve promising results,we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes.By multi-task training,our T-CNN aims to provide five facial landmarks,facial quality scores,and facial attributes like wearing sunglasses and wearing masks.Since there is no public facial quality data and facial attribute data as we need,we contribute two datasets,namely FaceQ and FaceA,which are collected from the Internet.Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark(FDDB),and gets reasonable results on AFLW,FaceQ and FaceA.展开更多
The incidence of dynamic coal or rock disasters is closely related to the distribution of stress in the surrounding rock. Our experiments show that electromagnetic radiation （EMR） signals are related to the state of...The incidence of dynamic coal or rock disasters is closely related to the distribution of stress in the surrounding rock. Our experiments show that electromagnetic radiation （EMR） signals are related to the state of stress of a coal body. The higher the stress, the more intense the deformation and fractures of a coal body and the stronger the EMR signals. EMR signals reflect the degrees of concentrated stress of a coal body and danger of a rock burst. We selected EMR intensity as the test index of the No.237 gob-surrounded coal face in the Nanshan coal mine. We tested the EMR characteristics of the stress distribution on the strike, on the incline and in the interior of the coal body. The EMR rule of rock bursts, caused by sudden changes in stress, is analyzed. Our research shows that EMR technology can be not only used to test qualitatively the stress distribution of the surrounding rock, but also to predict a possible occurrence of rock burst. Based on this, effective distress measures are used to eliminate or at least weaken the incidence of rock bursts. We hooe that safetv in coalmines will be enhanced.展开更多
Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. Thi...Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. This paper presents a robust, dense face detector using global context and visual attention mechanisms which can significantly improve detection accuracy. Specifically, a global context fusion module with top-down feedback is proposed to improve the ability to identify tiny faces. Moreover, a visual attention mechanism is employed to solve the problem of occlusion. Experimental results on the public face datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method.展开更多
An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigen...An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.展开更多
Face recognition is a kind of biometric technology that recognizes identities through human faces. At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition....Face recognition is a kind of biometric technology that recognizes identities through human faces. At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition. With the rapid development of deep learning and the application of Convolutional Neural Network (CNN) in the field of face recognition, the accuracy of face recognition has greatly improved. FaceNet is a deep learning framework commo</span><span><span style="font-family:Verdana;">nly used in face recognition in recent years. FaceNet uses the deep learning model GoogLeNet, which has </span><span style="font-family:Verdana;">a high</span><span style="font-family:Verdana;"> accuracy in face recognition. However, its network structure is too large, which causes the </span><span style="font-family:Verdana;">FaceNet</span><span style="font-family:Verdana;"> to run at a low speed. Therefore, to improve the running speed without affecting the recognition accuracy of FaceNet, this paper proposes a lightweight FaceNet model based on MobileNet. This article mainly does the following works:</span></span></span><span style="font-family:""> </span><span style="font-family:Verdana;">Based on the analysis of the low running speed of FaceNet and the principle of MobileNet, a lightweight FaceNet model based on MobileNet is proposed. The model would reduce the overall calculation of the network by using deep separable convolutio</span><span style="font-family:""><span style="font-family:Verdana;">ns. In this paper, the model is trained on the CASIA-WebFace and VGGFace2 </span><span style="font-family:Verdana;">datasets,</span><span style="font-family:Verdana;"> and tested on the LFW dataset. Experimental results show that the model reduces the network parameters to a large extent while ensuring </span><span style="font-family:Verdana;">the accuracy</span><span style="font-family:Verdana;"> and hence an increase in system computing speed. The model can also perform face recognition on a specific person in the video.展开更多
Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and agi...Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for both face identification and face verification. We also find that by using set-based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones.展开更多
In the present study a Genetic Programing model (GP) proposed for the prediction of relative crest settlement of concrete faced rock fill dams. To this end information of 30 large dams constructed in seven countries a...In the present study a Genetic Programing model (GP) proposed for the prediction of relative crest settlement of concrete faced rock fill dams. To this end information of 30 large dams constructed in seven countries across the world is gathered with their reported settlements. The results showed that the GP model is able to estimate the dam settlement properly based on four properties, void ratio of dam’s body (e), height (H), vertical deformation modulus (Ev) and shape factor (Sc) of the dam. For verification of the model applicability, obtained results compared with other research methods such as Clements’s formula and the finite element model. The comparison showed that in all cases the GP model led to be more accurate than those of performed in literature. Also a proper compatibility between the GP model and the finite element model was perceived.展开更多
Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security concerns.People are using face masks on a regular basis to protect themselves and to help reduce corona virus...Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security concerns.People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission.During the on-going coronavirus outbreak,one of the major priorities for researchers is to discover effective solution.As important parts of the face are obscured,face identification and verification becomes exceedingly difficult.The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,to identify the problem of face masked identification.In the first stage,we are applying face mask detector to identify the face mask.Then,the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10(CIFAR10),Modified National Institute of Standards and Technology Database(MNIST),Real World Masked Face Recognition Database(RMFRD),and Stimulated Masked Face Recognition Database(SMFRD).The proposed model is achieving recognition accuracy 99.82%with proposed dataset.This article employs the four pre-programmed models VGG16,VGG19,ResNet50 and ResNet101.To extract the deep features of faces with VGG16 is achieving 99.30%accuracy,VGG19 is achieving 99.54%accuracy,ResNet50 is achieving 78.70%accuracy and ResNet101 is achieving 98.64%accuracy with own dataset.The comparative analysis shows,that our proposed model performs better result in all four previous existing models.The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.展开更多
Low-resolution face images can be found in many practical applications. For example, faces captured from surveillance videos are typically in small sizes. Existing face recognition deep networks, trained on high-resol...Low-resolution face images can be found in many practical applications. For example, faces captured from surveillance videos are typically in small sizes. Existing face recognition deep networks, trained on high-resolution images, perform poorly in recognizing low-resolution faces. In this work, an improved multi-branch network is proposed by combining ResNet and feature super-resolution modules. ResNet is for recognizing high-resolution facial images and extracting features from both high-and low-resolution images.Feature super-resolution modules are inserted before the classifier of ResNet for low-resolution facial images. They are used to increase feature resolution. The proposed method is effective and simple. Experimental results show that the recognition accuracy for high-resolution face images is high, and the recognition accuracy for low-resolution face images is improved.展开更多
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita...Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.展开更多
Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”k...Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”kernel size of the convolutional operator,for the spatially dense patterns,such as the generic face images,the performance of CNNs is limited.Here,we propose a“non-local”model,termed the Speckle-Transformer(SpT)UNet,for speckle feature extraction of generic face images.It is worth noting that the lightweight SpT UNet reveals a high efficiency and strong comparative performance with Pearson Correlation Coefficient(PCC),and structural similarity measure(SSIM)exceeding 0.989,and 0.950,respectively.展开更多
The principles, methods, technologies and application effects of several electromagnetic methods for the detection of the hidden danger of water gushing at the coal face were introduced. Also, emphasis was laid on exp...The principles, methods, technologies and application effects of several electromagnetic methods for the detection of the hidden danger of water gushing at the coal face were introduced. Also, emphasis was laid on expounding the methods, principles and effects of down-hole detections by electric transmission tomography and transient electromagnetic method. The potential of point power supplied in the underground homogeneous semi-space, as well as the response to a low-resistivity abnormal body in the homogeneous semi-space, was simulated by adopting 3-D finite element method to interpret the basic theory of the electric transmission tomography. The results of actual measurement show that the mine electromagnetic method is sensitive to water-bearing low-resistivity bodies and can play a unique role in detecting the hidden danger of water gushing at the coal face.展开更多
As the use of facial attributes continues to expand,research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion,the age estimation of ...As the use of facial attributes continues to expand,research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion,the age estimation of faces is a challenging process.This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability.Improving face age estimation based on Soft Stagewise Regression Network(SSR-Net)and facial images,this paper employs the Center Symmetric Local Binary Pattern(CSLBP)method to obtain the feature image and then combines the face image and the feature image as network input data.Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness.The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.展开更多
As the information network plays a more and more important role globally, the traditional network theories and technologies, especially those related to network security, can no longer meet the network development req...As the information network plays a more and more important role globally, the traditional network theories and technologies, especially those related to network security, can no longer meet the network development requirements. Offering the system with secure and trusted services has become a new focus in network research. This paper first discusses the meaning of and aspects involved in the trusted network. According to this paper, the trusted network should be a network where the network’s and users’ behaviors and their results are always predicted and manageable. The trustworthiness of a network mainly involves three aspects: service provider, information transmission and terminal user. This paper also analyzes the trusted network in terms of trusted model for network/user behaviors, architecture of trusted network, service survivability and network manageability, which is designed to give ideas on solving the problems that may be faced in developing the trusted network.展开更多
Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. This paper reports the novel use and effectiveness of deep learning, in genera...Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. This paper reports the novel use and effectiveness of deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN architecture using the VGG-Face deep (neural network) learning is found to produce highly discriminative and interoperable features that are robust to aging variations even across a mix of biometric datasets. The features extracted show high inter-class and low intra-class variability leading to low generalization errors on aging datasets using ensembles of subspace discriminant classifiers. The classification results for the all-encompassing authentication methods proposed on the challenging FG-NET and MORPH datasets are competitive with state-of-the-art methods including commercial face recognition engines and are richer in functionality and interoperability than existing methods as it handles mixed biometric datasets, e.g., FG-NET and MORPH.展开更多
文摘Face antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems.Face recognition is commonly used for authentication in surveillance applications.However,attackers try to compromise these systems by using spoofing techniques such as using photos or videos of users to gain access to services or information.Many existing methods for face spoofing face difficulties when dealing with new scenarios,especially when there are variations in background,lighting,and other environmental factors.Recent advancements in deep learning with multi-modality methods have shown their effectiveness in face antispoofing,surpassing single-modal methods.However,these approaches often generate several features that can lead to issues with data dimensionality.In this study,we introduce a multimodal deep fusion network for face anti-spoofing that incorporates cross-axial attention and deep reinforcement learning techniques.This network operates at three patch levels and analyzes images from modalities(RGB,IR,and depth).Initially,our design includes an axial attention network(XANet)model that extracts deeply hidden features from multimodal images.Further,we use a bidirectional fusion technique that pays attention to both directions to combine features from each mode effectively.We further improve feature optimization by using the Enhanced Pity Beetle Optimization(EPBO)algorithm,which selects the features to address data dimensionality problems.Moreover,our proposed model employs a hybrid federated reinforcement learning(FDDRL)approach to detect and classify face anti-spoofing,achieving a more optimal tradeoff between detection rates and false positive rates.We evaluated the proposed approach on publicly available datasets,including CASIA-SURF and GREATFASD-S,and realized 98.985%and 97.956%classification accuracy,respectively.In addition,the current method outperforms other state-of-the-art methods in terms of precision,recall,and Fmeasures.Overall,the developed methodology boosts the effectiveness of our model in detecting various types of spoofing attempts.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549).
文摘The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.
基金funded by the Ministry of Public Security Science and Technology Program Project(No.2023LL35)the Key Laboratory of Smart Policing and National Security Risk Governance,Sichuan Province(No.ZHZZZD2302).
文摘As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many deep learning-based facial forgery detection approaches show promise,they often fail to delve deeply into the complex relationships between image features and forgery indicators,limiting their effectiveness to specific forgery techniques.To address this challenge,we propose a dual-branch collaborative deepfake detection network.The network processes video frame images as input,where a specialized noise extraction module initially extracts the noise feature maps.Subsequently,the original facial images and corresponding noise maps are directed into two parallel feature extraction branches to concurrently learn texture and noise forgery clues.An attention mechanism is employed between the two branches to facilitate mutual guidance and enhancement of texture and noise features across four different scales.This dual-modal feature integration enhances sensitivity to forgery artifacts and boosts generalization ability across various forgery techniques.Features from both branches are then effectively combined and processed through a multi-layer perception layer to distinguish between real and forged video.Experimental results on benchmark deepfake detection datasets demonstrate that our approach outperforms existing state-of-the-art methods in terms of detection performance,accuracy,and generalization ability.
文摘With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communication,image is widely used as a carrier of communication because of its rich content,intuitive and other advantages.Image recognition based on convolution neural network is the first application in the field of image recognition.A series of algorithm operations such as image eigenvalue extraction,recognition and convolution are used to identify and analyze different images.The rapid development of artificial intelligence makes machine learning more and more important in its research field.Use algorithms to learn each piece of data and predict the outcome.This has become an important key to open the door of artificial intelligence.In machine vision,image recognition is the foundation,but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition.Predecessors have provided many model algorithms,which have laid a solid foundation for the development of artificial intelligence and image recognition.The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network.Different from full connection network,convolutional neural network does not use full connection method in each layer of neurons of neural network,but USES some nodes for connection.Although this method reduces the computation time,due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation,this paper improves the model to be a multi-level information fusion of the convolution calculation method,and further recovers the discarded feature information,so as to improve the recognition rate of the image.VGG divides the network into five groups(mimicking the five layers of AlexNet),yet it USES 3*3 filters and combines them as a convolution sequence.Network deeper DCNN,channel number is bigger.The recognition rate of the model was verified by 0RL Face Database,BioID Face Database and CASIA Face Image Database.
基金supported by ZTE Corporation and State Key Laboratory of Mobile Network and Mobile Multimedia Technology
文摘This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists of a high-accuracy single stage detector(SSD)and an efficient tiny convolutional neural network(T-CNN)for joint face detection refinement,alignment and attribute analysis.Though the SSD face detectors achieve promising results,we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes.By multi-task training,our T-CNN aims to provide five facial landmarks,facial quality scores,and facial attributes like wearing sunglasses and wearing masks.Since there is no public facial quality data and facial attribute data as we need,we contribute two datasets,namely FaceQ and FaceA,which are collected from the Internet.Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark(FDDB),and gets reasonable results on AFLW,FaceQ and FaceA.
基金Projects 50204010 and 50427401 supported by the National Natural Science Foundation of China2005CB221505 by the National Basic Research Programof China2005BA813B-3-09 by the National "Tenth Five" Scientific and Technology Key Projects of China
文摘The incidence of dynamic coal or rock disasters is closely related to the distribution of stress in the surrounding rock. Our experiments show that electromagnetic radiation （EMR） signals are related to the state of stress of a coal body. The higher the stress, the more intense the deformation and fractures of a coal body and the stronger the EMR signals. EMR signals reflect the degrees of concentrated stress of a coal body and danger of a rock burst. We selected EMR intensity as the test index of the No.237 gob-surrounded coal face in the Nanshan coal mine. We tested the EMR characteristics of the stress distribution on the strike, on the incline and in the interior of the coal body. The EMR rule of rock bursts, caused by sudden changes in stress, is analyzed. Our research shows that EMR technology can be not only used to test qualitatively the stress distribution of the surrounding rock, but also to predict a possible occurrence of rock burst. Based on this, effective distress measures are used to eliminate or at least weaken the incidence of rock bursts. We hooe that safetv in coalmines will be enhanced.
基金supported by National Natural Science Foundation of China(No.61973009).
文摘Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. This paper presents a robust, dense face detector using global context and visual attention mechanisms which can significantly improve detection accuracy. Specifically, a global context fusion module with top-down feedback is proposed to improve the ability to identify tiny faces. Moreover, a visual attention mechanism is employed to solve the problem of occlusion. Experimental results on the public face datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method.
文摘An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.
文摘Face recognition is a kind of biometric technology that recognizes identities through human faces. At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition. With the rapid development of deep learning and the application of Convolutional Neural Network (CNN) in the field of face recognition, the accuracy of face recognition has greatly improved. FaceNet is a deep learning framework commo</span><span><span style="font-family:Verdana;">nly used in face recognition in recent years. FaceNet uses the deep learning model GoogLeNet, which has </span><span style="font-family:Verdana;">a high</span><span style="font-family:Verdana;"> accuracy in face recognition. However, its network structure is too large, which causes the </span><span style="font-family:Verdana;">FaceNet</span><span style="font-family:Verdana;"> to run at a low speed. Therefore, to improve the running speed without affecting the recognition accuracy of FaceNet, this paper proposes a lightweight FaceNet model based on MobileNet. This article mainly does the following works:</span></span></span><span style="font-family:""> </span><span style="font-family:Verdana;">Based on the analysis of the low running speed of FaceNet and the principle of MobileNet, a lightweight FaceNet model based on MobileNet is proposed. The model would reduce the overall calculation of the network by using deep separable convolutio</span><span style="font-family:""><span style="font-family:Verdana;">ns. In this paper, the model is trained on the CASIA-WebFace and VGGFace2 </span><span style="font-family:Verdana;">datasets,</span><span style="font-family:Verdana;"> and tested on the LFW dataset. Experimental results show that the model reduces the network parameters to a large extent while ensuring </span><span style="font-family:Verdana;">the accuracy</span><span style="font-family:Verdana;"> and hence an increase in system computing speed. The model can also perform face recognition on a specific person in the video.
文摘Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for both face identification and face verification. We also find that by using set-based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones.
文摘In the present study a Genetic Programing model (GP) proposed for the prediction of relative crest settlement of concrete faced rock fill dams. To this end information of 30 large dams constructed in seven countries across the world is gathered with their reported settlements. The results showed that the GP model is able to estimate the dam settlement properly based on four properties, void ratio of dam’s body (e), height (H), vertical deformation modulus (Ev) and shape factor (Sc) of the dam. For verification of the model applicability, obtained results compared with other research methods such as Clements’s formula and the finite element model. The comparison showed that in all cases the GP model led to be more accurate than those of performed in literature. Also a proper compatibility between the GP model and the finite element model was perceived.
文摘Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security concerns.People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission.During the on-going coronavirus outbreak,one of the major priorities for researchers is to discover effective solution.As important parts of the face are obscured,face identification and verification becomes exceedingly difficult.The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,to identify the problem of face masked identification.In the first stage,we are applying face mask detector to identify the face mask.Then,the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10(CIFAR10),Modified National Institute of Standards and Technology Database(MNIST),Real World Masked Face Recognition Database(RMFRD),and Stimulated Masked Face Recognition Database(SMFRD).The proposed model is achieving recognition accuracy 99.82%with proposed dataset.This article employs the four pre-programmed models VGG16,VGG19,ResNet50 and ResNet101.To extract the deep features of faces with VGG16 is achieving 99.30%accuracy,VGG19 is achieving 99.54%accuracy,ResNet50 is achieving 78.70%accuracy and ResNet101 is achieving 98.64%accuracy with own dataset.The comparative analysis shows,that our proposed model performs better result in all four previous existing models.The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.
文摘Low-resolution face images can be found in many practical applications. For example, faces captured from surveillance videos are typically in small sizes. Existing face recognition deep networks, trained on high-resolution images, perform poorly in recognizing low-resolution faces. In this work, an improved multi-branch network is proposed by combining ResNet and feature super-resolution modules. ResNet is for recognizing high-resolution facial images and extracting features from both high-and low-resolution images.Feature super-resolution modules are inserted before the classifier of ResNet for low-resolution facial images. They are used to increase feature resolution. The proposed method is effective and simple. Experimental results show that the recognition accuracy for high-resolution face images is high, and the recognition accuracy for low-resolution face images is improved.
基金supported by the National Natural Science Foundation of China(Nos.52279107 and 52379106)the Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co.,Ltd.,the Academician and Expert Workstation of Yunnan Province(No.202205AF150015)the Science and Technology Innovation Project of YCIC Group Co.,Ltd.(No.YCIC-YF-2022-15)。
文摘Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.
基金funding support from the Science and Technology Commission of Shanghai Municipality(Grant No.21DZ1100500)the Shanghai Frontiers Science Center Program(2021-2025 No.20)+2 种基金the Zhangjiang National Innovation Demonstration Zone(Grant No.ZJ2019ZD-005)supported by a fellowship from the China Postdoctoral Science Foundation(2020M671169)the International Postdoctoral Exchange Program from the Administrative Committee of Post-Doctoral Researchers of China([2020]33)。
文摘Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”kernel size of the convolutional operator,for the spatially dense patterns,such as the generic face images,the performance of CNNs is limited.Here,we propose a“non-local”model,termed the Speckle-Transformer(SpT)UNet,for speckle feature extraction of generic face images.It is worth noting that the lightweight SpT UNet reveals a high efficiency and strong comparative performance with Pearson Correlation Coefficient(PCC),and structural similarity measure(SSIM)exceeding 0.989,and 0.950,respectively.
基金Supported by the National Basic Research of China(2006CB202207)the National Natural Science Foundation of China(40674060)
文摘The principles, methods, technologies and application effects of several electromagnetic methods for the detection of the hidden danger of water gushing at the coal face were introduced. Also, emphasis was laid on expounding the methods, principles and effects of down-hole detections by electric transmission tomography and transient electromagnetic method. The potential of point power supplied in the underground homogeneous semi-space, as well as the response to a low-resistivity abnormal body in the homogeneous semi-space, was simulated by adopting 3-D finite element method to interpret the basic theory of the electric transmission tomography. The results of actual measurement show that the mine electromagnetic method is sensitive to water-bearing low-resistivity bodies and can play a unique role in detecting the hidden danger of water gushing at the coal face.
基金This work was funded by the foundation of Liaoning Educational committee under the Grant No.2019LNJC03.
文摘As the use of facial attributes continues to expand,research into facial age estimation is also developing.Because face images are easily affected by factors including illumination and occlusion,the age estimation of faces is a challenging process.This paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing ability.Improving face age estimation based on Soft Stagewise Regression Network(SSR-Net)and facial images,this paper employs the Center Symmetric Local Binary Pattern(CSLBP)method to obtain the feature image and then combines the face image and the feature image as network input data.Adding feature images to the convolutional neural network can improve the accuracy as well as increase the network model robustness.The experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.
基金the National NaturalScience Foundation of China under Grant90412012 and 60673187
文摘As the information network plays a more and more important role globally, the traditional network theories and technologies, especially those related to network security, can no longer meet the network development requirements. Offering the system with secure and trusted services has become a new focus in network research. This paper first discusses the meaning of and aspects involved in the trusted network. According to this paper, the trusted network should be a network where the network’s and users’ behaviors and their results are always predicted and manageable. The trustworthiness of a network mainly involves three aspects: service provider, information transmission and terminal user. This paper also analyzes the trusted network in terms of trusted model for network/user behaviors, architecture of trusted network, service survivability and network manageability, which is designed to give ideas on solving the problems that may be faced in developing the trusted network.
文摘Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. This paper reports the novel use and effectiveness of deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN architecture using the VGG-Face deep (neural network) learning is found to produce highly discriminative and interoperable features that are robust to aging variations even across a mix of biometric datasets. The features extracted show high inter-class and low intra-class variability leading to low generalization errors on aging datasets using ensembles of subspace discriminant classifiers. The classification results for the all-encompassing authentication methods proposed on the challenging FG-NET and MORPH datasets are competitive with state-of-the-art methods including commercial face recognition engines and are richer in functionality and interoperability than existing methods as it handles mixed biometric datasets, e.g., FG-NET and MORPH.
