Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significa...Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significant challenge.This study introduces ACE-YOLOX,a lightweight facial recognition model tailored for captive macaques.ACE-YOLOX incorporates Efficient Channel Attention(ECA),Complete Intersection over Union loss(CIoU),and Adaptive Spatial Feature Fusion(ASFF)into the YOLOX framework,enhancing prediction accuracy while reducing computational complexity.These integrated approaches enable effective multiscale feature extraction.Using a dataset comprising 179400 labeled facial images from 1196 macaques,ACE-YOLOX surpassed the performance of classical object detection models,demonstrating superior accuracy and real-time processing capabilities.An Android application was also developed to deploy ACE-YOLOX on smartphones,enabling on-device,real-time macaque recognition.Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool,offering an important foundation for future studies in macaque facial expression recognition,cognitive psychology,and social behavior.展开更多
Facial recognition payment(FRP),a new method of contactless payment,has attracted considerable attention over the past few years.However,the research on this topic remains nascent.This study assessed the drivers of cu...Facial recognition payment(FRP),a new method of contactless payment,has attracted considerable attention over the past few years.However,the research on this topic remains nascent.This study assessed the drivers of customers’FRP continuance intention from the perspectives of coolness and inspiration.We use online survey data from 610 Chinese FRP customers as the basis for our conceptual model.The results show that the coolness factors of subculture,attractiveness,utility,and originality have positive and significant effects on customers’inspired-by states and that subculture and utility also promote inspired-to.Inspired-by is positively associated with inspiredto,which in turn enhances customers’FRP continuance intention.Furthermore,the relationship between inspired-to and FRP continuance intention is negatively moderated by financial risk.In addition to contributing to the literature on FRP,coolness,and customer inspiration,this study offers several suggestions for implementing and developing FRP systems.展开更多
This study describes the development of a simple biometric facial recognition system, BFMT, which is designed for use in identifying individuals within a given population. The system is based on digital signatures der...This study describes the development of a simple biometric facial recognition system, BFMT, which is designed for use in identifying individuals within a given population. The system is based on digital signatures derived from facial images of human subjects. The results of the study demonstrate that a particular set of facial features from a simple two-dimensional image can yield a unique digital signature which can be used to identify a subject from a limited population within a controlled environment. The simplicity of the model upon which the system is based can result in commercial facial recognition systems that are more cost-effective to develop than those currently on the market.展开更多
The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced usin...The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.展开更多
An exhibition of ancient Greek artifacts,including a replica of the famed Agamemnon's Mask,sheds light on the similarities between the great ancient Bronze Age civilizations of China's Sanxingdui and Mycenae,a...An exhibition of ancient Greek artifacts,including a replica of the famed Agamemnon's Mask,sheds light on the similarities between the great ancient Bronze Age civilizations of China's Sanxingdui and Mycenae,and their use of gold in many artifacts.展开更多
The integration of Artificial Intelligence(AI)into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care.This comprehensive review explores the development,...The integration of Artificial Intelligence(AI)into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care.This comprehensive review explores the development,implementation,and challenges associated with AI-powered biometric facial identification in dairy agriculture.It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows,thereby facilitating thorough tracking of their health,behaviors,and productivity levels.Derived from facial recognition technologies originally designed for humans,this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations.The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks(CNNs)and deep learning frameworks signifies a transition toward data-driven agriculture.This analysis addresses notable challenges such as environmental variability,data collection difficulties,ethical considerations,and technological limitations.Furthermore,it compares various AI frameworks,highlighting their unique advantages and suitability in the dairy farming context.Despite these obstacles,facial recognition technology holds promise for enhancing farm efficiency,improving animal welfare,and promoting sustainable practices,underscoring the need for ongoing research and innovation.We advocate for future investigations focused on enhancing adaptability to diverse environments,ensuring ethical AI deployment,fostering compatibility across different breeds,and integrating with complementary agricultural technologies.Ultimately,this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices.展开更多
Artificial Intelligence(AI)constitutes a rapidly evolving set of technologies that offer significant economic,environmental,and societal benefits.However,the application of AI systems may also pose considerable risks ...Artificial Intelligence(AI)constitutes a rapidly evolving set of technologies that offer significant economic,environmental,and societal benefits.However,the application of AI systems may also pose considerable risks and inflict harm—whether material or immaterial,including physical,psychological,societal,or economic harm—to public interests and fundamental rights protected under Union law.展开更多
Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situ...Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.展开更多
A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree o...A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data.展开更多
Facial scanning is becoming more common as the commercial use of facial recognition technology expands.Face recognition technology,can be widely used in public security,finance,subway,airport and other important field...Facial scanning is becoming more common as the commercial use of facial recognition technology expands.Face recognition technology,can be widely used in public security,finance,subway,airport and other important fields of natural identification.Now,the technology has also been applied to the routine outbreak control and prevention,through the form of"face recognition"to bring more convenient,safer and more accurate experience.However,with the development of technology,the drawbacks of facial recognition are gradually revealed,and people's opinions on the technology are mixed.As facial recognition is widely used in the market,protecting users'privacy information and data is becoming an increasingly important issue.In this article,this paper will discuss the different factors contributing to the popularity of facial recognition among people from five aspects,respectively from the aspects of devices and people.This paper was covered a number of parts in this article to explore what factors influence the popularity of facial recognition,racially biased,Accuracy of identification,public acceptance,Personal experience with technology,public perception of face recognition technology and Alternatives to FRS.The conclusion is that the factors which most strongly impact on FR is accuracy.展开更多
Driven by the wave of digitalization,facial recognition technology has become a key tool for identity verification and security monitoring.However,with its widespread application in daily life,issues of personal priva...Driven by the wave of digitalization,facial recognition technology has become a key tool for identity verification and security monitoring.However,with its widespread application in daily life,issues of personal privacy security have also become prominent.This study delves into the complexities of facial recognition technology in the realm of personal information security through case analysis,revealing its convenience as an identity verification tool and the risks it poses as a potential means of privacy infringement.The research aims to identify and analyze the ethical and legal issues faced by facial recognition technology and to propose strategies for strengthening personal information protection.This paper integrates literature review,case studies,and international comparative analysis to provide support and recommendations for the practice and policy-making of personal information protection in the digital age.展开更多
Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a chal...Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).展开更多
In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression featu...In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system(FACS) and 'uniform' local binary patterns(LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models(ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood(K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience.展开更多
A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize huma...A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.展开更多
Functional magnetic resonance imaging was used during emotion recognition to identify changes in functional brain activation in 21 first-episode, treatment-naive major depressive disorder patients before and after ant...Functional magnetic resonance imaging was used during emotion recognition to identify changes in functional brain activation in 21 first-episode, treatment-naive major depressive disorder patients before and after antidepressant treatment. Following escitalopram oxalate treatment, patients exhibited decreased activation in bilateral precentral gyrus, bilateral middle frontal gyrus, left middle temporal gyrus, bilateral postcentral gyrus, left cingulate and right parahippocampal gyrus, and increased activation in right superior frontal gyrus, bilateral superior parietal Iobule and left occipital gyrus during sad facial expression recognition. After antidepressant treatment, patients also exhibited decreased activation in the bilateral middle frontal gyrus, bilateral cingulate and right parahippocampal gyrus, and increased activation in the right inferior frontal gyrus, left fusiform gyrus and right precuneus during happy facial expression recognition. Our experimental findings indicate that the limbic-cortical network might be a key target region for antidepressant treatment in major depressive disorder.展开更多
For the problems of complex model structure and too many training parameters in facial expression recognition algorithms,we proposed a residual network structure with a multi-headed channel attention(MCA)module.The mi...For the problems of complex model structure and too many training parameters in facial expression recognition algorithms,we proposed a residual network structure with a multi-headed channel attention(MCA)module.The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples.The designed MCA module is integrated into the ResNet18 backbone network.The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights,and the multi-head structure focuses more on the local features of the pictures,which improves the efficiency of facial expression recognition.Experimental results demonstrate that the model proposed in this paper achieves excellent recognition results in Fer2013,CK+and Jaffe datasets,with accuracy rates of 72.7%,98.8%and 93.33%,respectively.展开更多
Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER hav...Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.展开更多
In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According t...In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.展开更多
基金supported by the grants from Yunnan Province(202305AH340006,202305AH340007)CAS Light of West China Program(xbzg-zdsys-202213)。
文摘Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare.However,reliably identifying individual macaques in group environments remains a significant challenge.This study introduces ACE-YOLOX,a lightweight facial recognition model tailored for captive macaques.ACE-YOLOX incorporates Efficient Channel Attention(ECA),Complete Intersection over Union loss(CIoU),and Adaptive Spatial Feature Fusion(ASFF)into the YOLOX framework,enhancing prediction accuracy while reducing computational complexity.These integrated approaches enable effective multiscale feature extraction.Using a dataset comprising 179400 labeled facial images from 1196 macaques,ACE-YOLOX surpassed the performance of classical object detection models,demonstrating superior accuracy and real-time processing capabilities.An Android application was also developed to deploy ACE-YOLOX on smartphones,enabling on-device,real-time macaque recognition.Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool,offering an important foundation for future studies in macaque facial expression recognition,cognitive psychology,and social behavior.
基金This study was supported by the National Natural Science Foundation of China(72202185,72302145)the Postdoctoral Science Foundation of China(2023M742232)the Innovation Research 2035 Pilot Plan of Southwest University(SWUPilotPlan026).
文摘Facial recognition payment(FRP),a new method of contactless payment,has attracted considerable attention over the past few years.However,the research on this topic remains nascent.This study assessed the drivers of customers’FRP continuance intention from the perspectives of coolness and inspiration.We use online survey data from 610 Chinese FRP customers as the basis for our conceptual model.The results show that the coolness factors of subculture,attractiveness,utility,and originality have positive and significant effects on customers’inspired-by states and that subculture and utility also promote inspired-to.Inspired-by is positively associated with inspiredto,which in turn enhances customers’FRP continuance intention.Furthermore,the relationship between inspired-to and FRP continuance intention is negatively moderated by financial risk.In addition to contributing to the literature on FRP,coolness,and customer inspiration,this study offers several suggestions for implementing and developing FRP systems.
文摘This study describes the development of a simple biometric facial recognition system, BFMT, which is designed for use in identifying individuals within a given population. The system is based on digital signatures derived from facial images of human subjects. The results of the study demonstrate that a particular set of facial features from a simple two-dimensional image can yield a unique digital signature which can be used to identify a subject from a limited population within a controlled environment. The simplicity of the model upon which the system is based can result in commercial facial recognition systems that are more cost-effective to develop than those currently on the market.
基金This research was supported by the Science and Technology Department of Jilin Province[20210202128NC http://kjt.jl.gov.cn]The People’s Republic of China Ministry of Science and Technology[2018YFF0213606-03 http://www.most.gov.cn]+1 种基金the Jilin Province Development and Reform Commission[2019C021 http://jldrc.jl.gov.cn]the Science and Technology Bureau of Changchun City[21ZGN27 http://kjj.changchun.gov.cn].
文摘The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.
文摘An exhibition of ancient Greek artifacts,including a replica of the famed Agamemnon's Mask,sheds light on the similarities between the great ancient Bronze Age civilizations of China's Sanxingdui and Mycenae,and their use of gold in many artifacts.
文摘The integration of Artificial Intelligence(AI)into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care.This comprehensive review explores the development,implementation,and challenges associated with AI-powered biometric facial identification in dairy agriculture.It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows,thereby facilitating thorough tracking of their health,behaviors,and productivity levels.Derived from facial recognition technologies originally designed for humans,this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations.The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks(CNNs)and deep learning frameworks signifies a transition toward data-driven agriculture.This analysis addresses notable challenges such as environmental variability,data collection difficulties,ethical considerations,and technological limitations.Furthermore,it compares various AI frameworks,highlighting their unique advantages and suitability in the dairy farming context.Despite these obstacles,facial recognition technology holds promise for enhancing farm efficiency,improving animal welfare,and promoting sustainable practices,underscoring the need for ongoing research and innovation.We advocate for future investigations focused on enhancing adaptability to diverse environments,ensuring ethical AI deployment,fostering compatibility across different breeds,and integrating with complementary agricultural technologies.Ultimately,this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices.
文摘Artificial Intelligence(AI)constitutes a rapidly evolving set of technologies that offer significant economic,environmental,and societal benefits.However,the application of AI systems may also pose considerable risks and inflict harm—whether material or immaterial,including physical,psychological,societal,or economic harm—to public interests and fundamental rights protected under Union law.
基金supported by China Academy of Railway Sciences Corporation Limited(No.2021YJ127).
文摘Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.
基金The National Natural Science Foundation of China (No.60503023,60872160)the Natural Science Foundation for Universities ofJiangsu Province (No.08KJD520009)the Intramural Research Foundationof Nanjing University of Information Science and Technology(No.Y603)
文摘A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data.
文摘Facial scanning is becoming more common as the commercial use of facial recognition technology expands.Face recognition technology,can be widely used in public security,finance,subway,airport and other important fields of natural identification.Now,the technology has also been applied to the routine outbreak control and prevention,through the form of"face recognition"to bring more convenient,safer and more accurate experience.However,with the development of technology,the drawbacks of facial recognition are gradually revealed,and people's opinions on the technology are mixed.As facial recognition is widely used in the market,protecting users'privacy information and data is becoming an increasingly important issue.In this article,this paper will discuss the different factors contributing to the popularity of facial recognition among people from five aspects,respectively from the aspects of devices and people.This paper was covered a number of parts in this article to explore what factors influence the popularity of facial recognition,racially biased,Accuracy of identification,public acceptance,Personal experience with technology,public perception of face recognition technology and Alternatives to FRS.The conclusion is that the factors which most strongly impact on FR is accuracy.
文摘Driven by the wave of digitalization,facial recognition technology has become a key tool for identity verification and security monitoring.However,with its widespread application in daily life,issues of personal privacy security have also become prominent.This study delves into the complexities of facial recognition technology in the realm of personal information security through case analysis,revealing its convenience as an identity verification tool and the risks it poses as a potential means of privacy infringement.The research aims to identify and analyze the ethical and legal issues faced by facial recognition technology and to propose strategies for strengthening personal information protection.This paper integrates literature review,case studies,and international comparative analysis to provide support and recommendations for the practice and policy-making of personal information protection in the digital age.
基金supported by the Academy of Finland(267581)the D2I SHOK Project from Digile Oy as well as Nokia Technologies(Tampere,Finland)
文摘Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).
基金supported by National Natural Science Foundation of China(No.61273339)
文摘In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system(FACS) and 'uniform' local binary patterns(LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models(ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood(K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience.
基金supported by the National Natural Science Foundation of China(61403422,61273102)the Hubei Provincial Natural Science Foundation of China(2015CFA010)+1 种基金the Ⅲ Project(B17040)the Fundamental Research Funds for National University,China University of Geosciences(Wuhan)
文摘A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.
基金supported by research grants from the National Natural Science Foundation of China (No. 81071099)the Liaoning Science and Technology Foundation (No. 2008225010-14)Doctoral Foundation of the First Affiliated Hospital in China Medical University (No. 2010)
文摘Functional magnetic resonance imaging was used during emotion recognition to identify changes in functional brain activation in 21 first-episode, treatment-naive major depressive disorder patients before and after antidepressant treatment. Following escitalopram oxalate treatment, patients exhibited decreased activation in bilateral precentral gyrus, bilateral middle frontal gyrus, left middle temporal gyrus, bilateral postcentral gyrus, left cingulate and right parahippocampal gyrus, and increased activation in right superior frontal gyrus, bilateral superior parietal Iobule and left occipital gyrus during sad facial expression recognition. After antidepressant treatment, patients also exhibited decreased activation in the bilateral middle frontal gyrus, bilateral cingulate and right parahippocampal gyrus, and increased activation in the right inferior frontal gyrus, left fusiform gyrus and right precuneus during happy facial expression recognition. Our experimental findings indicate that the limbic-cortical network might be a key target region for antidepressant treatment in major depressive disorder.
基金funded by Anhui Province Quality Engineering Project No.2021jyxm0801Natural Science Foundation of Anhui University of Chinese Medicine under Grant Nos.2020zrzd18,2019zrzd11+1 种基金Humanity Social Science foundation Grants 2021rwzd20,2020rwzd07Anhui University of Chinese Medicine Quality Engineering Projects No.2021zlgc046.
文摘For the problems of complex model structure and too many training parameters in facial expression recognition algorithms,we proposed a residual network structure with a multi-headed channel attention(MCA)module.The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples.The designed MCA module is integrated into the ResNet18 backbone network.The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights,and the multi-head structure focuses more on the local features of the pictures,which improves the efficiency of facial expression recognition.Experimental results demonstrate that the model proposed in this paper achieves excellent recognition results in Fer2013,CK+and Jaffe datasets,with accuracy rates of 72.7%,98.8%and 93.33%,respectively.
文摘Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.
文摘In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.
