Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex ...Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex lighting,and complex structure of face anti-spoofing detection network,a novel method is presented using a combination of convolutional neural network and brightness equalization.Firstly,multi-task convolutional neural network(MTCNN)based on the cascade of three convolutional neural networks(CNNs),P-net,R-net,and O-net are used to achieve accurate positioning of the face,and the detected face bounding box is cropped by a specified multiple,then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image.Finally,data features are extracted and classification is given by utilizing a 12-layer convolution neural network.Experiments of the proposed algorithm were carried out on CASIA-FASD.The results show that the classification accuracy is relatively high,and the half total error rate(HTER)reaches 1.02%.展开更多
The economic and scientific value that small celestial bodies(SCBs)offer humanity is the main motivation for close exploration of these bodies.However,autonomous optical navigation is challenging due to the light vari...The economic and scientific value that small celestial bodies(SCBs)offer humanity is the main motivation for close exploration of these bodies.However,autonomous optical navigation is challenging due to the light variation caused by the rapid spin of SCBs.In this context,we propose a light prior brightness equalization self-calibration method,which can achieve brightness equalization of SCB images under varying illumination conditions while preserving image details,thereby increasing the number of feature-matching points.First,we design a light prior information function based on the illumination variation law of Lambert’s cosine law.Based on the function,the high-light and low-light areas of SCB images are distinguished.Furthermore,we create a brightness equalization mathematical model that maps the illumination components of high-light and low-light areas.Then,based on the brightness equalization mathematical model,we construct a light prior brightness self-calibration network.The proposed network includes 3 main modules:the illumination component estimation module,brightness self-calibration module,and light prior information prediction module;the proposed network utilizes a multistage illumination sharing approach to achieve separation and optimization of illumination components.Finally,the experimental results show that our method can achieve brightness equalization,markedly increasing the number of correct feature matches.展开更多
基金Project(61671204)supported by National Natural Science Foundation of ChinaProject(2016WK2001)supported by Hunan Provincial Key R&D Plan,China。
文摘Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex lighting,and complex structure of face anti-spoofing detection network,a novel method is presented using a combination of convolutional neural network and brightness equalization.Firstly,multi-task convolutional neural network(MTCNN)based on the cascade of three convolutional neural networks(CNNs),P-net,R-net,and O-net are used to achieve accurate positioning of the face,and the detected face bounding box is cropped by a specified multiple,then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image.Finally,data features are extracted and classification is given by utilizing a 12-layer convolution neural network.Experiments of the proposed algorithm were carried out on CASIA-FASD.The results show that the classification accuracy is relatively high,and the half total error rate(HTER)reaches 1.02%.
基金support from the National Natural Science Foundation of China(Grant No.U2341214)the Shandong Provincial Natural Science Foundation,China(ZR2023MF006 and ZR2023QF176)the Stable Support Program(HTKJ2024KL502028).
文摘The economic and scientific value that small celestial bodies(SCBs)offer humanity is the main motivation for close exploration of these bodies.However,autonomous optical navigation is challenging due to the light variation caused by the rapid spin of SCBs.In this context,we propose a light prior brightness equalization self-calibration method,which can achieve brightness equalization of SCB images under varying illumination conditions while preserving image details,thereby increasing the number of feature-matching points.First,we design a light prior information function based on the illumination variation law of Lambert’s cosine law.Based on the function,the high-light and low-light areas of SCB images are distinguished.Furthermore,we create a brightness equalization mathematical model that maps the illumination components of high-light and low-light areas.Then,based on the brightness equalization mathematical model,we construct a light prior brightness self-calibration network.The proposed network includes 3 main modules:the illumination component estimation module,brightness self-calibration module,and light prior information prediction module;the proposed network utilizes a multistage illumination sharing approach to achieve separation and optimization of illumination components.Finally,the experimental results show that our method can achieve brightness equalization,markedly increasing the number of correct feature matches.
