Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
As an important sustainable energy source,Li-ion batteries have been widely used in mobile phones,electric vehicles,large-scale energy storage and aerospace.However,due to the inevitable safety risks of traditional li...As an important sustainable energy source,Li-ion batteries have been widely used in mobile phones,electric vehicles,large-scale energy storage and aerospace.However,due to the inevitable safety risks of traditional liquid Li-ion batteries,the use of all-solid-state batteries to replace organic liquid electrolytes has become one of the most effective ways to solve safety problem.Solid-state electrolyte(SSE)is the core part of allsolid-state Li-ion battery,and ideal SSE has the characteristics of high ionic conductivity,wide enough electrochemical stability window,suitable mechanical strength and excellent chemical stability,the first among which is particularly an essential prerequisite.While,so far only a few SSEs exhibit the Li ionic conductivities higher than 10^(-4) S/cm at room temperature.展开更多
The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys....The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys.To address the issues of high construction costs and the need to replace equipment for different processes,this paper designs an economical and practical multi-functional integrated trolley based on engineering cases.This trolley is suitable for various construction methods such as full-face excavation and benching method,and integrates functions such as drilling and blasting holes,anchor bolt holes,advance grouting holes,pipe roof construction,charging,anchor bolt installation and grouting,and arch mesh installation.It reduces the number of operators,improves the tunnel working environment,lowers construction costs,and enhances construction efficiency.展开更多
All-solid-state lithium ion batteries(ASSLIBs)have attracted much attention due to their high safety and increased energy density,which have become a substitute to conventional liquid electrolyte batteries[1].The deve...All-solid-state lithium ion batteries(ASSLIBs)have attracted much attention due to their high safety and increased energy density,which have become a substitute to conventional liquid electrolyte batteries[1].The development of high-performance solid electrolyte is the key to the development of solid-state battery technology.Solid-state electrolyte(SSE)materials should have high ionic conductivity,poor electronic conductivity,wide electrochemical window,and low electrode and electrolyte interface resistance.展开更多
巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image ...巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image J软件即可对细胞进行提取和量化。研究表明,eMLC在炎症情况下均可出现细胞募集及活化现象,但在不同眼底病中各具特点。在糖尿病视网膜病变、视网膜静脉阻塞等视网膜缺血缺氧性疾病中,eMLC密度越高,黄斑水肿可能越严重。此外,eMLC密度更高的视网膜静脉阻塞患者抗VEGF疗效更差,视力预后不佳,提示基于en face OCT的eMLC不仅可用于评估视网膜炎症情况,而且还能充当提示疾病疗效及预后的标志物。在葡萄膜炎等免疫炎症性疾病中,en face OCT亦可观测到eMLC密度、形态等改变。白塞病葡萄膜炎患者视网膜血管渗漏程度与eMLC密度相关性强,故eMLC密度可充当无创评估视网膜血管渗漏程度的新指标。然而,目前提取和量化eMLC的方法及标准不统一,降低了各研究间的可比性。因此,亟需制定统一的操作规范和评估标准。此外eMLC所代表的具体细胞类型及功能仍需进一步探究。未来,研究者可以利用en face OCT对眼底炎症地进行无创评估。基于en face OCT的eMLC还能作为基础研究与临床研究之间的桥梁,为揭示疾病的致病机制提供重要参考。展开更多
This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,an...This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.展开更多
Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for...Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for further investigation,and the specifically exposed crystal faces of CeO_(2)may have an impact on the performance of nitrogen doped CeO_(2).Herein,nitrogen-doped CeO_(2)with different morphologies and exposed crystal faces was prepared,and its performances in the photocatalytic degradation of tetracycline(TC)or hydrogen production via water splitting were evaluated.Density functional theory(DFT)was used to simulate the band structures,density of states,and oxygen defect properties of different CeO_(2)structures.It was found that nitrogen doping and OVs synergistically promoted the catalytic activity of nitrogen-doped CeO_(2).In addition,the exposed crystal faces of CeO_(2)have significant effects on the introduction of nitrogen and the ease of OV generation,as well as the synergistic effect of nitrogen doping with OVs.Among them,the rod-like nitrogen-doped CeO_(2)with exposed(110)face(R-CeO_(2)-NH_(3))showed a photocatalytic degradation ratio of 73.59%for TC and hydrogen production of 156.89μmol/g,outperforming other prepared photocatalysts.展开更多
In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers...In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers in the experiment.In this paper,a simple attack method called incomplete physical adversarial attack(IPAA)is proposed to simulate physical attacks.Different from the process of physical attacks,when an IPAA is conducted,a photo of the adversarial sticker is embedded into a facial image as the input to attack FR systems,which can obtain results similar to those of physical attacks without inviting any volunteers.The results show that IPAA has a higher similarity with physical attacks than digital attacks,indicating that IPAA is able to evaluate the performance of physical attacks.IPAA is effective in quantitatively measuring the impact of the sticker location on the results of attacks.展开更多
With extensive attention being paid to the potential environmental hazards of discarded face masks,catalytic pyrolysis technologies have been proposed to realize the valorization of wastes.However,recent catalyst sele...With extensive attention being paid to the potential environmental hazards of discarded face masks,catalytic pyrolysis technologies have been proposed to realize the valorization of wastes.However,recent catalyst selection and system design have focused solely on conversion efficiency,ignoring economic cost and potential life-cycle environmental damage.Here,we propose an economic-environmental hybrid pre-assessment method to help identify catalysts and reactors with less environmental impact and high economic returns among various routes to convert discarded face masks into carbon nanotubes(CNTs)and hydrogen.In catalyst selection,it was found that a widely known Fe-Ni catalyst exhibits higher catalytic activity than a cheaper Fe catalyst,potentially increasing the economic viability of the catalytic pyrolysis system by 38%-55%.The use of this catalyst also results in a carbon reduction of 4.12-10.20kilogram CO_(2) equivalent for 1 kilogram of discarded face masks,compared with the cheaper Fe catalyst.When the price of CNTs exceeds 1.49×10^(4) USD·t^(-1),microwave-assisted pyrolysis is the optimal choice due to its superior environmental performance(in terms of its life-cycle greenhouse gas reduction potential,eutrophication potential,and human toxicity)and economic benefits.In contrast,conventional heating pyrolysis may be a more economical option due to its good stability over 43 reaction regeneration cycles,as compared with a microwave-assisted pyrolysis catalyst with a higher conversion efficiency.This study connects foundational science with ecological economics to guide emerging technologies in their research stage toward technical efficiency,economic benefits,and environmental sustainability.展开更多
In interpersonal communication,the principle of politeness is an important communicative principle that is widely applied in people’s daily life.However,the communication patterns of the principle of politeness and f...In interpersonal communication,the principle of politeness is an important communicative principle that is widely applied in people’s daily life.However,the communication patterns of the principle of politeness and face theory in the travel planning process among friends still need further exploration.This study aims to analyze the specific manifestations of these principles in the communication patterns of travel planning among friends through a pragmatic interpretation of the principle of politeness and face theory,providing a new perspective for understanding linguistic behavior in interpersonal relationships.展开更多
Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extrac...Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.展开更多
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting...Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting faces with high levels of occlusion,such as those covered by masks,veils,or scarves,remains a significant challenge,as traditional models often fail to generalize under such conditions.This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients(HOG)and Canny edge detection with modern deep learning models.The goal is to improve face detection accuracy under occlusions.The proposed method leverages the structural strengths of HOG and edge-based object proposals while exploiting the feature extraction capabilities of Convolutional Neural Networks(CNNs).The effectiveness of the proposed model is assessed using a custom dataset containing 10,000 heavily occluded face images and a subset of the Common Objects in Context(COCO)dataset for non-face samples.The COCO dataset was selected for its variety and realism in background contexts.Experimental evaluations demonstrate significant performance improvements compared to baseline CNN models.Results indicate that DenseNet121 combined with HOG outperforms other counterparts in classification metrics with an F1-score of 87.96%and precision of 88.02%.Enhanced performance is achieved through reduced false positives and improved localization accuracy with the integration of object proposals based on Canny and contour detection.While the proposed method increases inference time from 33.52 to 97.80 ms,it achieves a notable improvement in precision from 80.85% to 88.02% when comparing the baseline DenseNet121 model to its hybrid counterpart.Limitations of the method include higher computational cost and the need for careful tuning of parameters across the edge detection,handcrafted features,and CNN components.These findings highlight the potential of combining handcrafted and learned features for occluded face detection tasks.展开更多
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金supported by the Natural Science Foundation of Shandong Province(No.ZR2020MB049)the Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai(No.AMGM2023A07)。
文摘As an important sustainable energy source,Li-ion batteries have been widely used in mobile phones,electric vehicles,large-scale energy storage and aerospace.However,due to the inevitable safety risks of traditional liquid Li-ion batteries,the use of all-solid-state batteries to replace organic liquid electrolytes has become one of the most effective ways to solve safety problem.Solid-state electrolyte(SSE)is the core part of allsolid-state Li-ion battery,and ideal SSE has the characteristics of high ionic conductivity,wide enough electrochemical stability window,suitable mechanical strength and excellent chemical stability,the first among which is particularly an essential prerequisite.While,so far only a few SSEs exhibit the Li ionic conductivities higher than 10^(-4) S/cm at room temperature.
文摘The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys.To address the issues of high construction costs and the need to replace equipment for different processes,this paper designs an economical and practical multi-functional integrated trolley based on engineering cases.This trolley is suitable for various construction methods such as full-face excavation and benching method,and integrates functions such as drilling and blasting holes,anchor bolt holes,advance grouting holes,pipe roof construction,charging,anchor bolt installation and grouting,and arch mesh installation.It reduces the number of operators,improves the tunnel working environment,lowers construction costs,and enhances construction efficiency.
文摘All-solid-state lithium ion batteries(ASSLIBs)have attracted much attention due to their high safety and increased energy density,which have become a substitute to conventional liquid electrolyte batteries[1].The development of high-performance solid electrolyte is the key to the development of solid-state battery technology.Solid-state electrolyte(SSE)materials should have high ionic conductivity,poor electronic conductivity,wide electrochemical window,and low electrode and electrolyte interface resistance.
文摘巨噬细胞样细胞(macrophage-like cells, MLC)指起源、功能与巨噬细胞类似的免疫细胞,包括小胶质细胞、玻璃体细胞及巨噬细胞。将en face OCT显示层面设置在视网膜表明即可观测到视网膜表明的MLC(epiretinal MLC, eMLC),随后利用Image J软件即可对细胞进行提取和量化。研究表明,eMLC在炎症情况下均可出现细胞募集及活化现象,但在不同眼底病中各具特点。在糖尿病视网膜病变、视网膜静脉阻塞等视网膜缺血缺氧性疾病中,eMLC密度越高,黄斑水肿可能越严重。此外,eMLC密度更高的视网膜静脉阻塞患者抗VEGF疗效更差,视力预后不佳,提示基于en face OCT的eMLC不仅可用于评估视网膜炎症情况,而且还能充当提示疾病疗效及预后的标志物。在葡萄膜炎等免疫炎症性疾病中,en face OCT亦可观测到eMLC密度、形态等改变。白塞病葡萄膜炎患者视网膜血管渗漏程度与eMLC密度相关性强,故eMLC密度可充当无创评估视网膜血管渗漏程度的新指标。然而,目前提取和量化eMLC的方法及标准不统一,降低了各研究间的可比性。因此,亟需制定统一的操作规范和评估标准。此外eMLC所代表的具体细胞类型及功能仍需进一步探究。未来,研究者可以利用en face OCT对眼底炎症地进行无创评估。基于en face OCT的eMLC还能作为基础研究与临床研究之间的桥梁,为揭示疾病的致病机制提供重要参考。
基金Supported by the Fundamental Research Funds for the Central Universities(2024300443)the Natural Science Foundation of Jiangsu Province(BK20241224).
文摘This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.
基金Project(52164025)supported by the National Natural Science Foundation of ChinaProject([2020]1Y219)supported by the Basic Research Program from the Science&Technology Department of Guizhou Province,China+2 种基金Project([2019]30)supported by the Training Project from Guizhou University,ChinaProject([2023]04)supported by the Guizhou University Innovation Talent Team Project,ChinaProject([2022]041)supported by the Natural Science Research Project of Guizhou Provincial Department of Education,China。
文摘Nitrogen doping has significant effects on the photocatalytic performance of ceria(CeO_(2)),and the possible synergistic effect with the inevitably introduced abundant oxygen vacancies(OVs)is of great significance for further investigation,and the specifically exposed crystal faces of CeO_(2)may have an impact on the performance of nitrogen doped CeO_(2).Herein,nitrogen-doped CeO_(2)with different morphologies and exposed crystal faces was prepared,and its performances in the photocatalytic degradation of tetracycline(TC)or hydrogen production via water splitting were evaluated.Density functional theory(DFT)was used to simulate the band structures,density of states,and oxygen defect properties of different CeO_(2)structures.It was found that nitrogen doping and OVs synergistically promoted the catalytic activity of nitrogen-doped CeO_(2).In addition,the exposed crystal faces of CeO_(2)have significant effects on the introduction of nitrogen and the ease of OV generation,as well as the synergistic effect of nitrogen doping with OVs.Among them,the rod-like nitrogen-doped CeO_(2)with exposed(110)face(R-CeO_(2)-NH_(3))showed a photocatalytic degradation ratio of 73.59%for TC and hydrogen production of 156.89μmol/g,outperforming other prepared photocatalysts.
文摘In recent work,adversarial stickers are widely used to attack face recognition(FR)systems in the physical world.However,it is difficult to evaluate the performance of physical attacks because of the lack of volunteers in the experiment.In this paper,a simple attack method called incomplete physical adversarial attack(IPAA)is proposed to simulate physical attacks.Different from the process of physical attacks,when an IPAA is conducted,a photo of the adversarial sticker is embedded into a facial image as the input to attack FR systems,which can obtain results similar to those of physical attacks without inviting any volunteers.The results show that IPAA has a higher similarity with physical attacks than digital attacks,indicating that IPAA is able to evaluate the performance of physical attacks.IPAA is effective in quantitatively measuring the impact of the sticker location on the results of attacks.
基金supported by the National Natural Science Foundation of China(52076099,52306257,and 72293601)。
文摘With extensive attention being paid to the potential environmental hazards of discarded face masks,catalytic pyrolysis technologies have been proposed to realize the valorization of wastes.However,recent catalyst selection and system design have focused solely on conversion efficiency,ignoring economic cost and potential life-cycle environmental damage.Here,we propose an economic-environmental hybrid pre-assessment method to help identify catalysts and reactors with less environmental impact and high economic returns among various routes to convert discarded face masks into carbon nanotubes(CNTs)and hydrogen.In catalyst selection,it was found that a widely known Fe-Ni catalyst exhibits higher catalytic activity than a cheaper Fe catalyst,potentially increasing the economic viability of the catalytic pyrolysis system by 38%-55%.The use of this catalyst also results in a carbon reduction of 4.12-10.20kilogram CO_(2) equivalent for 1 kilogram of discarded face masks,compared with the cheaper Fe catalyst.When the price of CNTs exceeds 1.49×10^(4) USD·t^(-1),microwave-assisted pyrolysis is the optimal choice due to its superior environmental performance(in terms of its life-cycle greenhouse gas reduction potential,eutrophication potential,and human toxicity)and economic benefits.In contrast,conventional heating pyrolysis may be a more economical option due to its good stability over 43 reaction regeneration cycles,as compared with a microwave-assisted pyrolysis catalyst with a higher conversion efficiency.This study connects foundational science with ecological economics to guide emerging technologies in their research stage toward technical efficiency,economic benefits,and environmental sustainability.
文摘In interpersonal communication,the principle of politeness is an important communicative principle that is widely applied in people’s daily life.However,the communication patterns of the principle of politeness and face theory in the travel planning process among friends still need further exploration.This study aims to analyze the specific manifestations of these principles in the communication patterns of travel planning among friends through a pragmatic interpretation of the principle of politeness and face theory,providing a new perspective for understanding linguistic behavior in interpersonal relationships.
基金the financial support from Natural Science Foundation of Gansu Province(Nos.22JR5RA217,22JR5RA216)Lanzhou Science and Technology Program(No.2022-2-111)+1 种基金Lanzhou University of Arts and Sciences School Innovation Fund Project(No.XJ2022000103)Lanzhou College of Arts and Sciences 2023 Talent Cultivation Quality Improvement Project(No.2023-ZL-jxzz-03)。
文摘Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project Grant Number(BFP/RGP/ICT/22/490).
文摘Face detection is a critical component inmodern security,surveillance,and human-computer interaction systems,with widespread applications in smartphones,biometric access control,and public monitoring.However,detecting faces with high levels of occlusion,such as those covered by masks,veils,or scarves,remains a significant challenge,as traditional models often fail to generalize under such conditions.This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients(HOG)and Canny edge detection with modern deep learning models.The goal is to improve face detection accuracy under occlusions.The proposed method leverages the structural strengths of HOG and edge-based object proposals while exploiting the feature extraction capabilities of Convolutional Neural Networks(CNNs).The effectiveness of the proposed model is assessed using a custom dataset containing 10,000 heavily occluded face images and a subset of the Common Objects in Context(COCO)dataset for non-face samples.The COCO dataset was selected for its variety and realism in background contexts.Experimental evaluations demonstrate significant performance improvements compared to baseline CNN models.Results indicate that DenseNet121 combined with HOG outperforms other counterparts in classification metrics with an F1-score of 87.96%and precision of 88.02%.Enhanced performance is achieved through reduced false positives and improved localization accuracy with the integration of object proposals based on Canny and contour detection.While the proposed method increases inference time from 33.52 to 97.80 ms,it achieves a notable improvement in precision from 80.85% to 88.02% when comparing the baseline DenseNet121 model to its hybrid counterpart.Limitations of the method include higher computational cost and the need for careful tuning of parameters across the edge detection,handcrafted features,and CNN components.These findings highlight the potential of combining handcrafted and learned features for occluded face detection tasks.
