Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve sa...Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve satisfactory results.In this paper,we propose a face recognition algorithm that combines the traditional features and deep features of masked faces.For traditional features,we extract Local Binary Pattern(LBP),Scale-Invariant Feature Transform(SIFT)and Histogram of Oriented Gradient(HOG)features from the periocular region,and use the Support Vector Machines(SVM)classifier to perform personal identification.We also propose an improved Convolutional Neural Network(CNN)model Angular Visual Geometry Group Network(A-VGG)to learn deep features.Then we use the decision-level fusion to combine the four features.Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces,including frontal and side faces taken at different angles.Images with motion blur were also tested to evaluate the robustness of the algorithm.Besides,the experiment of matching a masked face with the corresponding full face is accomplished.The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition,and the periocular region has rich biological features and high discrimination.展开更多
With the rapid development of road traffic,the number of high-speed rail passengers is huge,and the flow of people is dense.In epidemic situation,it is prone to intensive infection in high-speed rail carriages,which i...With the rapid development of road traffic,the number of high-speed rail passengers is huge,and the flow of people is dense.In epidemic situation,it is prone to intensive infection in high-speed rail carriages,which is not conducive to national prevention and control work.Based on face recognition technology,the intelligent service robot for high-speed rail passengers walks in accordance with the set route and detects the face mask of high-speed rail passengers.The face database of high-speed rail passengers is compared in real time.The passengers who do not wear masks are reminded in time to reduce the risk of infection.Moreover,the robot can accurately remind the passengers of leaving the station in time,and has the functions of automatic selling and student ticket checking.The experimental result is shown to promote the further development of high-speed rail services.展开更多
基金Supported by the Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(XCXJH20220318)。
文摘Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve satisfactory results.In this paper,we propose a face recognition algorithm that combines the traditional features and deep features of masked faces.For traditional features,we extract Local Binary Pattern(LBP),Scale-Invariant Feature Transform(SIFT)and Histogram of Oriented Gradient(HOG)features from the periocular region,and use the Support Vector Machines(SVM)classifier to perform personal identification.We also propose an improved Convolutional Neural Network(CNN)model Angular Visual Geometry Group Network(A-VGG)to learn deep features.Then we use the decision-level fusion to combine the four features.Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces,including frontal and side faces taken at different angles.Images with motion blur were also tested to evaluate the robustness of the algorithm.Besides,the experiment of matching a masked face with the corresponding full face is accomplished.The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition,and the periocular region has rich biological features and high discrimination.
基金This article is supported by the 2020 Innovation and Entrepreneurship Training Program forCollege Students in Jiangsu Province(Project name:high-speed railway passenger behavior management system,No.202011460091T)This article is supported by the National Natural Science Foundation of China Youth Science Foundation project(Project name:research on Deep Discriminant Spares Representation Learning Method for Feature Extraction,No.61806098)This article is supported by Scientific Research Project of Nanjing Xiaozhuang University(Project name:multi-robot collaborative system,No.2017NXY16)。
文摘With the rapid development of road traffic,the number of high-speed rail passengers is huge,and the flow of people is dense.In epidemic situation,it is prone to intensive infection in high-speed rail carriages,which is not conducive to national prevention and control work.Based on face recognition technology,the intelligent service robot for high-speed rail passengers walks in accordance with the set route and detects the face mask of high-speed rail passengers.The face database of high-speed rail passengers is compared in real time.The passengers who do not wear masks are reminded in time to reduce the risk of infection.Moreover,the robot can accurately remind the passengers of leaving the station in time,and has the functions of automatic selling and student ticket checking.The experimental result is shown to promote the further development of high-speed rail services.
