The Experts below are selected from a list of 70629 Experts worldwide ranked by ideXlab platform
Huansheng Ning - One of the best experts on this subject based on the ideXlab platform.
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A unified Face Identification and resolution scheme using cloud computing in Internet of Things
Future Generation Computer Systems, 2018Co-Authors: Huansheng Ning, Tie Qiu, Xiong Luo, Arun Kumar SangaiahAbstract:Abstract In the Internet of Things (IoT), Identification and resolution of physical object is crucial for authenticating object’s identity, controlling service access, and establishing trust between object and cloud service. With the development of computer vision and pattern recognition technologies, Face has been used as a high-security Identification and identity authentication method which has been deployed in various applications. Face Identification can ensure the consistency between individual in physical-space and his/her identity in cyber-space during the physical–cyber space mapping. However, Face is a non-code and unstructured identifier. With the increase of applications in current big data environment, the characteristic of Face Identification will result in the growing demands for computation power and storage capacity. In this paper, we propose a Face Identification and resolution scheme based on cloud computing to solve the above problem. The Face Identification and resolution system model is presented to introduce the processes of Face identifier generation and matching. Then, parallel matching mechanism and cloud computing-based resolution framework are proposed to efficiently resolve Face image, control personal data access and acquire individual’s identity information. It makes full use of the advantages of cloud computing to effectively improve computation power and storage capacity. The experimental result of prototype system indicates that the proposed scheme is practically feasible and can provide efficient Face Identification and resolution service.
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security and privacy preservation scheme of Face Identification and resolution framework using fog computing in internet of things
IEEE Internet of Things Journal, 2017Co-Authors: Huansheng Ning, Tie Qiu, Houbing Song, Yanna Wang, Xuanxia YaoAbstract:Face Identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on Face Identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based Face Identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based Face Identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of Face Identification and Face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.
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Fog Computing Based Face Identification and Resolution Scheme in Internet of Things
IEEE Transactions on Industrial Informatics, 2017Co-Authors: Huansheng Ning, Tie Qiu, Yanfei Zhang, Xiong LuoAbstract:The Identification and resolution technology are the prerequisite for realizing identity consistency of physical–cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in Identification applications. With the increase of Face Identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based Face Identification and resolution scheme. Face identifier is first generated by the Identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of Face Identification and resolution.
Tie Qiu - One of the best experts on this subject based on the ideXlab platform.
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A unified Face Identification and resolution scheme using cloud computing in Internet of Things
Future Generation Computer Systems, 2018Co-Authors: Huansheng Ning, Tie Qiu, Xiong Luo, Arun Kumar SangaiahAbstract:Abstract In the Internet of Things (IoT), Identification and resolution of physical object is crucial for authenticating object’s identity, controlling service access, and establishing trust between object and cloud service. With the development of computer vision and pattern recognition technologies, Face has been used as a high-security Identification and identity authentication method which has been deployed in various applications. Face Identification can ensure the consistency between individual in physical-space and his/her identity in cyber-space during the physical–cyber space mapping. However, Face is a non-code and unstructured identifier. With the increase of applications in current big data environment, the characteristic of Face Identification will result in the growing demands for computation power and storage capacity. In this paper, we propose a Face Identification and resolution scheme based on cloud computing to solve the above problem. The Face Identification and resolution system model is presented to introduce the processes of Face identifier generation and matching. Then, parallel matching mechanism and cloud computing-based resolution framework are proposed to efficiently resolve Face image, control personal data access and acquire individual’s identity information. It makes full use of the advantages of cloud computing to effectively improve computation power and storage capacity. The experimental result of prototype system indicates that the proposed scheme is practically feasible and can provide efficient Face Identification and resolution service.
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security and privacy preservation scheme of Face Identification and resolution framework using fog computing in internet of things
IEEE Internet of Things Journal, 2017Co-Authors: Huansheng Ning, Tie Qiu, Houbing Song, Yanna Wang, Xuanxia YaoAbstract:Face Identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on Face Identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based Face Identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based Face Identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of Face Identification and Face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.
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Fog Computing Based Face Identification and Resolution Scheme in Internet of Things
IEEE Transactions on Industrial Informatics, 2017Co-Authors: Huansheng Ning, Tie Qiu, Yanfei Zhang, Xiong LuoAbstract:The Identification and resolution technology are the prerequisite for realizing identity consistency of physical–cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in Identification applications. With the increase of Face Identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based Face Identification and resolution scheme. Face identifier is first generated by the Identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of Face Identification and resolution.
Xiong Luo - One of the best experts on this subject based on the ideXlab platform.
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A unified Face Identification and resolution scheme using cloud computing in Internet of Things
Future Generation Computer Systems, 2018Co-Authors: Huansheng Ning, Tie Qiu, Xiong Luo, Arun Kumar SangaiahAbstract:Abstract In the Internet of Things (IoT), Identification and resolution of physical object is crucial for authenticating object’s identity, controlling service access, and establishing trust between object and cloud service. With the development of computer vision and pattern recognition technologies, Face has been used as a high-security Identification and identity authentication method which has been deployed in various applications. Face Identification can ensure the consistency between individual in physical-space and his/her identity in cyber-space during the physical–cyber space mapping. However, Face is a non-code and unstructured identifier. With the increase of applications in current big data environment, the characteristic of Face Identification will result in the growing demands for computation power and storage capacity. In this paper, we propose a Face Identification and resolution scheme based on cloud computing to solve the above problem. The Face Identification and resolution system model is presented to introduce the processes of Face identifier generation and matching. Then, parallel matching mechanism and cloud computing-based resolution framework are proposed to efficiently resolve Face image, control personal data access and acquire individual’s identity information. It makes full use of the advantages of cloud computing to effectively improve computation power and storage capacity. The experimental result of prototype system indicates that the proposed scheme is practically feasible and can provide efficient Face Identification and resolution service.
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Fog Computing Based Face Identification and Resolution Scheme in Internet of Things
IEEE Transactions on Industrial Informatics, 2017Co-Authors: Huansheng Ning, Tie Qiu, Yanfei Zhang, Xiong LuoAbstract:The Identification and resolution technology are the prerequisite for realizing identity consistency of physical–cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in Identification applications. With the increase of Face Identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based Face Identification and resolution scheme. Face identifier is first generated by the Identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of Face Identification and resolution.
Xuanxia Yao - One of the best experts on this subject based on the ideXlab platform.
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security and privacy preservation scheme of Face Identification and resolution framework using fog computing in internet of things
IEEE Internet of Things Journal, 2017Co-Authors: Huansheng Ning, Tie Qiu, Houbing Song, Yanna Wang, Xuanxia YaoAbstract:Face Identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on Face Identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based Face Identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based Face Identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of Face Identification and Face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.
Andrew Beng Jin Teoh - One of the best experts on this subject based on the ideXlab platform.
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Open-set Face Identification with index-of-max hashing by learning
Pattern Recognition, 2020Co-Authors: Xingbo Dong, Soohyung Kim, Zhe Jin, Jung Yeon Hwang, Sangrae Cho, Andrew Beng Jin TeohAbstract:Abstract Large-scale Face Identification or 1-to-N matching where N is huge, plays a vital role in biometrics and surveillance. The system demands accurate and speedy matching where compact facial feature representation and a simple matcher are favored. On the other hand, most research considers closed-set Identification that assumes that all identities of probe samples are enclosed in the gallery. On the contrary, open-set Identification expects that some probe identities are not known to the system. This setup poses an additional challenge, where the system should be able to reject those probes that correspond to unknown identities. In this paper, we address the large-scale open-set Face Identification problem with a compact facial representation that is based on the index-of-maximum (IoM) hashing, which was designed for biometric template protection. To be specific, the existing random IoM hashing is advanced to a data-driven based hashing technique, where the hashed Face code can be made compact and matching can be easily performed by the Hamming distance, which can offer highly efficient matching. Furthermore, since IoM hashing transforms the original facial features non-invertibly, the privacy of users can also be preserved. Along with IoM hashed Face code, we explore several fusion strategies to address the open-set Face Identification problem. The comprehensive evaluations are carried out with three large-scale unconstrained Face datasets, namely LFW, VGG2 and IJB-C.
