Aim to countermeasure the presentation attack for iris recognition system,an iris liveness detection scheme based on batch normalized convolutional neural network(BNCNN)is proposed to improve the reliability of the ir...Aim to countermeasure the presentation attack for iris recognition system,an iris liveness detection scheme based on batch normalized convolutional neural network(BNCNN)is proposed to improve the reliability of the iris authentication system.The BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris,including convolutional layer,batch-normalized(BN)layer,Relu layer,pooling layer and full connected layer.The iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels,and then the iris features are extracted by BNCNN.With these features,the genuine iris and fake iris are determined by the decision-making layer.Batch normalization technique is used in BNCNN to avoid the problem of over fitting and gradient disappearing during training.Extensive experiments are conducted on three classical databases:the CASIA Iris Lamp database,the CASIA Iris Syn database and Ndcontact database.The results show that the proposed method can effectively extract micro texture features of the iris,and achieve higher detection accuracy compared with some typical iris liveness detection methods.展开更多
In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dime...In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.展开更多
Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the...Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the residual module is extended to three dimensions,which can extract features in the time and space domain at the same time.Second,by changing the size of the pooling layer window the integrity of the time domain features is preserved,at the same time,in order to overcome the difficulty of network training and over-fitting problems,the batch normalization(BN)layer and the dropout layer are added.After that,because the global average pooling layer(GAP)is affected by the size of the feature map,the network cannot be further deepened,so the convolution layer and maxpool layer are added to the R3D network.Finally,because LSTM has the ability to memorize information and can extract more abstract timing features,the LSTM network is introduced into the R3D network.Experimental results show that the R3D+LSTM network achieves 91%recognition rate on the UCF-101 dataset.展开更多
基于深度学习的加密流量分类方法中的分类模型大多是深层直筒型结构,存在梯度消失的问题,且网络层数的增加会使模型结构和计算的复杂度显著上升。为此,提出了一种基于改进Inception-ResNet的加密流量分类方法。该方法通过改进Inception...基于深度学习的加密流量分类方法中的分类模型大多是深层直筒型结构,存在梯度消失的问题,且网络层数的增加会使模型结构和计算的复杂度显著上升。为此,提出了一种基于改进Inception-ResNet的加密流量分类方法。该方法通过改进Inception模块,并将该模块作为残差块以残差结构连接的方式嵌入卷积神经网络来构建分类模型;此外,改进分类模型的损失函数,并使用VPN-nonVPN数据集来验证所提方法的有效性。实验结果表明,所提方法在2种场景的分类实验中的精确率、召回率、F1值分别达到了94.21%、92.53%和93.31%以上。在与其他方法的对比实验中,以分类难度最大的12分类实验为例,所提方法比C4.5决策树算法和1D-CNN(1 Dimensional-Convolutional Neural Network)在精确率上分别高出13.91和9.50个百分点,在召回率上分别高出14.87和1.59个百分点。与CAE(Convolutional Auto Encoding)和SAE (Stacked Auto Encoder)等方法相比,所提方法虽然在各项指标上没有明显提升,但在单次训练时长上却有明显缩短,充分表明了所提方法的先进性。展开更多
基金This work was supported in part by project supported by National Natural Science Foundation of China(Grant No.61572182,No.61370225)project supported by Hunan Provincial Natural Science Foundation of China(Grant No.15JJ2007).
文摘Aim to countermeasure the presentation attack for iris recognition system,an iris liveness detection scheme based on batch normalized convolutional neural network(BNCNN)is proposed to improve the reliability of the iris authentication system.The BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris,including convolutional layer,batch-normalized(BN)layer,Relu layer,pooling layer and full connected layer.The iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels,and then the iris features are extracted by BNCNN.With these features,the genuine iris and fake iris are determined by the decision-making layer.Batch normalization technique is used in BNCNN to avoid the problem of over fitting and gradient disappearing during training.Extensive experiments are conducted on three classical databases:the CASIA Iris Lamp database,the CASIA Iris Syn database and Ndcontact database.The results show that the proposed method can effectively extract micro texture features of the iris,and achieve higher detection accuracy compared with some typical iris liveness detection methods.
基金the National Natural Science Foundation of China(No.61772417,61634004,61602377)Key R&D Program Projects in Shaanxi Province(No.2017GY-060)Shaanxi Natural Science Basic Research Project(No.2018JM4018).
文摘In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.
基金Supported by the Shaanxi Province Key Research and Development Project (No. 2021GY-280)Shaanxi Province Natural Science Basic Research Program (No. 2021JM-459)the National Natural Science Foundation of China (No. 61772417)
文摘Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the residual module is extended to three dimensions,which can extract features in the time and space domain at the same time.Second,by changing the size of the pooling layer window the integrity of the time domain features is preserved,at the same time,in order to overcome the difficulty of network training and over-fitting problems,the batch normalization(BN)layer and the dropout layer are added.After that,because the global average pooling layer(GAP)is affected by the size of the feature map,the network cannot be further deepened,so the convolution layer and maxpool layer are added to the R3D network.Finally,because LSTM has the ability to memorize information and can extract more abstract timing features,the LSTM network is introduced into the R3D network.Experimental results show that the R3D+LSTM network achieves 91%recognition rate on the UCF-101 dataset.
文摘基于深度学习的加密流量分类方法中的分类模型大多是深层直筒型结构,存在梯度消失的问题,且网络层数的增加会使模型结构和计算的复杂度显著上升。为此,提出了一种基于改进Inception-ResNet的加密流量分类方法。该方法通过改进Inception模块,并将该模块作为残差块以残差结构连接的方式嵌入卷积神经网络来构建分类模型;此外,改进分类模型的损失函数,并使用VPN-nonVPN数据集来验证所提方法的有效性。实验结果表明,所提方法在2种场景的分类实验中的精确率、召回率、F1值分别达到了94.21%、92.53%和93.31%以上。在与其他方法的对比实验中,以分类难度最大的12分类实验为例,所提方法比C4.5决策树算法和1D-CNN(1 Dimensional-Convolutional Neural Network)在精确率上分别高出13.91和9.50个百分点,在召回率上分别高出14.87和1.59个百分点。与CAE(Convolutional Auto Encoding)和SAE (Stacked Auto Encoder)等方法相比,所提方法虽然在各项指标上没有明显提升,但在单次训练时长上却有明显缩短,充分表明了所提方法的先进性。
