ECG Biometric Authentication Based On Feature Learning

With the rapid development of social information data,information security of human beings are facing the huge security risk.As the new identification technology with high security and uniqueness,biometric identification technology is gradually entering the public.As a new biometric identification technology,ECG signal has simple preprocessing,easy collection and difficult falsification characteristics and gradually become a research hotspot in the field of biometric authentication.The technology not only promotes the rapid development of the field of biometric authentication,but also effectively complements the existing biometric identification technology.Although many technologies have made breakthroughs in the respect so far,there are still some problems of low identification precision and bad efficiency.To solve these problems,the paper researches the feature extraction of heart beats and feature learning.In order to be closer to the actual application,the sources of data are not be restricted that include heart rate,physical condition and emotional state of every individual.In process of the feature extraction,according to the signal sampling frequency,frequency characteristics of ECG signal and noise,the paper adopts wavelet denoising of nine layer to obtain the pure signal.Then we use two-order difference threshold method to detect heart beats and extract the morphological features of signal and wavelet feature.In order to obtain the optimal heart beats features for classification,the experimental contrast for different classifier has been made.Compared with the single morphological features(dimension is 425,heart beat classification accuracy is 74%,identification accuracy is 90%)and wavelet features(dimension is 172,heart beat classification accuracy is 72%,identification accuracy is 93%),the compound feature(dimension is 624,heart beat classification accuracy is 76%,identification accuracy is 93%)could achieve higher classification accuracy.While the compound feature improves identification accuracy as input feature for system,the sharp increasing of feature dimension leading too much feature redundancy which causes high complexity and low efficiency of identification system.To solve this problem,the paper uses kernel principal component analysis(KPCA)to make up the deficiency of linear transform PCA which couldn’t express the intrinsic connection among nonlinear signal.We realize that KPCA algorithm(dimension is 500,heart beat classification accuracy is 76%,identification accuracy is 94%)could reduce feature dimension and improve system efficiency without affecting the classification accuracy.But KPCA algorithm is not suitable for the practical application of ECG identification,the paper adopts feature learning network to further improve system efficiency.Firstly the paper uses sparse autoencoder to set initial of feature learning network and utilizes global parameter tuning to improve the recognition performance of the network.At last,we adopt L-BFGS algorithm to optimize network parameters and reduce time complexity and space complexity of ECG feature learning algorithm.Finally,compared with KPCA algorithm,the feature learning network(dimension is 50,heart beat classification accuracy is 87%,identification accuracy is 96%)not only can effectively reduce feature dimension and improve identification accuracy through experiments.So it ensures the accuracy,efficiency and robustness of authentication system.