Non-face-to-face psychological counseling systems rely on network technologies to anonymize information regard-ing client identity.However,these systems often face challenges concerning voice data leaks and the subopt...Non-face-to-face psychological counseling systems rely on network technologies to anonymize information regard-ing client identity.However,these systems often face challenges concerning voice data leaks and the suboptimal communication of the client’s non-verbal expressions,such as facial cues,to the counselor.This study proposes a metaverse-based psychological counseling system designed to enhance client identity protection while ensuring efficient information delivery to counselors during non-face-to-face counseling.The proposed systemincorporates a voicemodulation function that instantlymodifies/masks the client’s voice to safeguard their identity.Additionally,it employs real-time client facial expression recognition using an ensemble of decision trees to mirror the client’s non-verbal expressions through their avatar in the metaverse environment.The system is adaptable for use on personal computers and smartphones,offering users the flexibility to access metaverse-based psychological counseling across diverse environments.The performance evaluation of the proposed system confirmed that the voice modulation and real-time facial expression replication consistently achieve an average speed of 48.32 frames per second or higher,even when tested on the least powerful smartphone configurations.Moreover,a total of 550 actual psychological counseling sessions were conducted,and the average satisfaction rating reached 4.46 on a 5-point scale.This indicates that clients experienced improved identity protection compared to conventional non-face-to-face metaverse counseling approaches.Additionally,the counselor successfully addressed the challenge of conveying non-verbal cues from clients who typically struggled with non-face-to-face psychological counseling.The proposed systemholds significant potential for applications in interactive discussions and educational activities in the metaverse.展开更多
With the widespread deployment of biometric recognition,personal data security and privacy are attracted more and more attentions.A crucial privacy issue is how to ensure the security of user template.This paper propo...With the widespread deployment of biometric recognition,personal data security and privacy are attracted more and more attentions.A crucial privacy issue is how to ensure the security of user template.This paper proposes a novel template protection algorithm for face recognition based on chaotic map.Each face template is corresponding to different chaotic sequence produced by system master key and user identification number.The order of chaotic sequence controls the substitution index of face template.Experiment results on facial FERET database show that our algorithm can significantly improve the recognition performance and ensure the security of face template.展开更多
Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it...Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.展开更多
In order to safely exploit coal resource, protection coal pillars must be prepared in coal mines. Some correlative parameters of protection coal pillar are calculated by Drop face and Drop line methods. Models of prot...In order to safely exploit coal resource, protection coal pillars must be prepared in coal mines. Some correlative parameters of protection coal pillar are calculated by Drop face and Drop line methods. Models of protecting surface objects and coal pillars are established by TIN modeling and object-oriented technique. By using ACCESS2000as the database and the VC++ and OpenGL as the language, the calculation of protective coal pillars is realized and the 3D-visulizaiton system for protected objects on ground surface and for coal pillars is developed. The system can obtain the data of characteristic points on the surface interactively from the digitized mine topography map, constructing 3D model automatically. It can also obtain the interrelated parameters of the coal seam and drill hole data from existing geological surveying database to calculate the location, surface area and the total coal columns. The whole process can be computed quickly and accurately. And the 3D visualization system was applied in a mine, showing that the system solve the problem of complex calculation,not only realized the automatic 3D mapping and visualization of coal pillars for buildings protection , but also greatly improves the working efficiency.展开更多
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-004)supported by the Technology Development Program(S3230339)funded by the Ministry of SMEs and Startups(MSS,Korea).
文摘Non-face-to-face psychological counseling systems rely on network technologies to anonymize information regard-ing client identity.However,these systems often face challenges concerning voice data leaks and the suboptimal communication of the client’s non-verbal expressions,such as facial cues,to the counselor.This study proposes a metaverse-based psychological counseling system designed to enhance client identity protection while ensuring efficient information delivery to counselors during non-face-to-face counseling.The proposed systemincorporates a voicemodulation function that instantlymodifies/masks the client’s voice to safeguard their identity.Additionally,it employs real-time client facial expression recognition using an ensemble of decision trees to mirror the client’s non-verbal expressions through their avatar in the metaverse environment.The system is adaptable for use on personal computers and smartphones,offering users the flexibility to access metaverse-based psychological counseling across diverse environments.The performance evaluation of the proposed system confirmed that the voice modulation and real-time facial expression replication consistently achieve an average speed of 48.32 frames per second or higher,even when tested on the least powerful smartphone configurations.Moreover,a total of 550 actual psychological counseling sessions were conducted,and the average satisfaction rating reached 4.46 on a 5-point scale.This indicates that clients experienced improved identity protection compared to conventional non-face-to-face metaverse counseling approaches.Additionally,the counselor successfully addressed the challenge of conveying non-verbal cues from clients who typically struggled with non-face-to-face psychological counseling.The proposed systemholds significant potential for applications in interactive discussions and educational activities in the metaverse.
文摘With the widespread deployment of biometric recognition,personal data security and privacy are attracted more and more attentions.A crucial privacy issue is how to ensure the security of user template.This paper proposes a novel template protection algorithm for face recognition based on chaotic map.Each face template is corresponding to different chaotic sequence produced by system master key and user identification number.The order of chaotic sequence controls the substitution index of face template.Experiment results on facial FERET database show that our algorithm can significantly improve the recognition performance and ensure the security of face template.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R442),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.
基金Projects 59904001 supported by National Natural Science Foundation of China
文摘In order to safely exploit coal resource, protection coal pillars must be prepared in coal mines. Some correlative parameters of protection coal pillar are calculated by Drop face and Drop line methods. Models of protecting surface objects and coal pillars are established by TIN modeling and object-oriented technique. By using ACCESS2000as the database and the VC++ and OpenGL as the language, the calculation of protective coal pillars is realized and the 3D-visulizaiton system for protected objects on ground surface and for coal pillars is developed. The system can obtain the data of characteristic points on the surface interactively from the digitized mine topography map, constructing 3D model automatically. It can also obtain the interrelated parameters of the coal seam and drill hole data from existing geological surveying database to calculate the location, surface area and the total coal columns. The whole process can be computed quickly and accurately. And the 3D visualization system was applied in a mine, showing that the system solve the problem of complex calculation,not only realized the automatic 3D mapping and visualization of coal pillars for buildings protection , but also greatly improves the working efficiency.
