Fingerprint Liveness Detection Based On Texture And Deep Learning

Fingerprint liveness detection has been widely used in access control,cell phone un-locking,payment and the second generation ID card.Using biometric features such as fingerprint to authenticate takes advantage of the uniqueness of individual biological infor-mation.Iris and fingerprints are unique to everyone.Besides,they are simple to operate but difficult to duplicate.The fingerprint liveness detection has been an active research topic in the past few years,and it has been proved that the standard optical and capacitive sensors can be used for counterfeiting and deception.Fingerprint based authentication system needs to create a way to distinguish between genuine and fake fingerprints.To solve this problem,this paper studies fingerprint detection from two aspects:traditional texture and current popular deep learning.The main contents of this paper are as follows:(1)The original fingerprint image often has a lot of noise and the fingerprint image contains a large number of blank foreground images.For the problems stated above,this paper advocates to using the proposed fingerprint image cutting operator to cut the finger-print image first,then carrying out denoising and other preprocessing.In addition,in order to make use of the contribution of the sharp information such as the noise component to the detection of the fingerprint,not only guided filtering but also the cutting image is used to do denoising,then the CoALBP feature extraction operator is used to extract the features of the two types of images.The experimental results show that the proposed detection method can effectively detect the genuine and fake fingerprints,and enhance the security of the fingerprint identification system.(2)Realizing the great advantage of the convolution neural network in the field of image recognition,this paper uses the convolution neural network to carry out the migra-tion application on the fingerprint liveness detection,and classifies the fingerprint into two classifications.At the same time,combined with the traditional computer vision image pre-processing method,the fingerprint image is first tailored and denoised.On this basis,the processed image is partitioned and trained using the proposed network model.Finally,the model,which has been trained last step,is used to judge the blocks of the single test image and the voting strategy is used to ultimately determine the whole image's class.(3)A fingerprint detection simulation system has been designed and implemented.It has shown a better application scene in improving the detection performance and security.