Expression detection plays a vital role to determine the patient’s condition in healthcare systems.It helps the monitoring teams to respond swiftly in case of emergency.Due to the lack of suitable methods,results are...Expression detection plays a vital role to determine the patient’s condition in healthcare systems.It helps the monitoring teams to respond swiftly in case of emergency.Due to the lack of suitable methods,results are often compromised in an unconstrained environment because of pose,scale,occlusion and illumination variations in the image of the face of the patient.A novel patch-based multiple local binary patterns(LBP)feature extraction technique is proposed for analyzing human behavior using facial expression recognition.It consists of three-patch[TPLBP]and four-patch LBPs[FPLBP]based feature engineering respectively.Image representation is encoded from local patch statistics using these descriptors.TPLBP and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based methods.Coded images are transformed into the frequency domain using a discrete cosine transform(DCT).Most discriminant features extracted from coded DCT images are combined to generate a feature vector.Support vector machine(SVM),k-nearest neighbor(KNN),and Naïve Bayes(NB)are used for the classification of facial expressions using selected features.Extensive experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade(CK+)and Oulu–CASIA datasets.Results demonstrate that the proposed methodology outperforms the other techniques used for comparison.展开更多
Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled regions.In addition,they have been deployed in civilian critical infrastructure monitoring.In this environm...Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled regions.In addition,they have been deployed in civilian critical infrastructure monitoring.In this environment,real-time and massive data is exchanged between the aerial vehicles and the ground control stations.Depending on the mission of these aerial vehicles,some of the collected and transmitted data is sensitive and private.Therefore,many security protocols have been presented to offer privacy and security protection.However,majority of these schemes fail to consider attack vectors such as side-channeling,de-synchronization and known secret session temporary information leakages.This last attack can be launched upon adversarial physical capture of these drones.In addition,some of these protocols deploy computationally intensive asymmetric cryptographic primitives that result in high overheads.In this paper,an authentication protocol based on lightweight quadratic residues and hash functions is developed.Its formal security analysis is executed using the widely deployed random oracle model.In addition,informal security analysis is carried out to show its robustness under the Dolev–Yao(DY)and Canetti–Krawczyk(CK)threat models.In terms of operational efficiency,it is shown to have relatively lower execution time,communication costs,and incurs the least storage costs among other related protocols.Specifically,the proposed protocol provides a 25%improvement in supported security and privacy features and a 6.52%reduction in storage costs.In overall,the proposed methodology offers strong security and privacy protection at lower execution time,storage and communication overheads.展开更多
基金supported in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP2020-2016-0-00312)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation)and in part by the Faculty Research Fund of Sejong University in 2019.
文摘Expression detection plays a vital role to determine the patient’s condition in healthcare systems.It helps the monitoring teams to respond swiftly in case of emergency.Due to the lack of suitable methods,results are often compromised in an unconstrained environment because of pose,scale,occlusion and illumination variations in the image of the face of the patient.A novel patch-based multiple local binary patterns(LBP)feature extraction technique is proposed for analyzing human behavior using facial expression recognition.It consists of three-patch[TPLBP]and four-patch LBPs[FPLBP]based feature engineering respectively.Image representation is encoded from local patch statistics using these descriptors.TPLBP and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based methods.Coded images are transformed into the frequency domain using a discrete cosine transform(DCT).Most discriminant features extracted from coded DCT images are combined to generate a feature vector.Support vector machine(SVM),k-nearest neighbor(KNN),and Naïve Bayes(NB)are used for the classification of facial expressions using selected features.Extensive experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade(CK+)and Oulu–CASIA datasets.Results demonstrate that the proposed methodology outperforms the other techniques used for comparison.
文摘Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled regions.In addition,they have been deployed in civilian critical infrastructure monitoring.In this environment,real-time and massive data is exchanged between the aerial vehicles and the ground control stations.Depending on the mission of these aerial vehicles,some of the collected and transmitted data is sensitive and private.Therefore,many security protocols have been presented to offer privacy and security protection.However,majority of these schemes fail to consider attack vectors such as side-channeling,de-synchronization and known secret session temporary information leakages.This last attack can be launched upon adversarial physical capture of these drones.In addition,some of these protocols deploy computationally intensive asymmetric cryptographic primitives that result in high overheads.In this paper,an authentication protocol based on lightweight quadratic residues and hash functions is developed.Its formal security analysis is executed using the widely deployed random oracle model.In addition,informal security analysis is carried out to show its robustness under the Dolev–Yao(DY)and Canetti–Krawczyk(CK)threat models.In terms of operational efficiency,it is shown to have relatively lower execution time,communication costs,and incurs the least storage costs among other related protocols.Specifically,the proposed protocol provides a 25%improvement in supported security and privacy features and a 6.52%reduction in storage costs.In overall,the proposed methodology offers strong security and privacy protection at lower execution time,storage and communication overheads.
