Detecting Iris Liveness with Batch Normalized Convolutional Neural Network

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.

Secure and Anonymous Three-Factor Authentication Scheme for Remote Healthcare Systems

Wireless medical sensor networks(WMSNs)play a significant role in increasing the availability of remote healthcare systems.The vital and physiological data of the patient can be collected using the WMSN via sensor nodes that are placed on his/her body and then transmitted remotely to a healthcare professional for proper diagnosis.The protection of the patient’s privacy and their data from unauthorized access is a major concern in such systems.Therefore,an authentication scheme with a high level of security is one of the most effective mechanisms by which to address these security concerns.Many authentication schemes for remote patient monitoring have been proposed recently.However,the majority of these schemes are extremely vulnerable to attacks and are unsuitable for practical use.This paper proposes a secure three-factor authentication scheme for a patient-monitoring healthcare system that operates remotely using a WMSN.The proposed authentication scheme is formally verified using the Burrows,Abadi and Needham’s(BAN)logic model and an automatic cryptographic protocol verifier(ProVerif)tool.We show that our authentication scheme can prevent relevant types of security breaches in a practical context according to the discussed possible attack scenarios.Comparisons of the security and performance are carried out with recently proposed authentication schemes.The results of the analysis show that the proposed authentication scheme is secure and practical for use,with reasonable storage space,computation,and communication efficiency.