期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Detecting Iris Liveness with Batch Normalized Convolutional Neural Network 被引量:2
1
作者 Min Long Yan Zeng 《Computers, Materials & Continua》 SCIE EI 2019年第2期493-504,共12页
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
关键词 Iris liveness detection batch normalization convolutional neural network biometric feature recognition
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部