Study On Identity Recognition Algorithm Based On Photoplethysmography Signal

As the modern information society develops rapidly,the secure communication of public information has been greatly challenged.It is becoming more and more important to develop new technology or doing new researches to ensure secure communication.Biometric is a technology which can automatically identify the individual identity through the physiological signals or unique characteristics of the human behavior.The custom authentication methods which are based on faces,fingerprints or iris are widely used in financial transactions and other fields.However,there are more and more problems in these methods and they cannot meet the requirements of the situation which need high information security.As inherent physiological signals,ECG signal,Photoplethysmography signal and respiratory signal can reflect individual unique characteristics,and that they could be easily acquired.They have great application value and a wide research space in the field of identification.In this paper,two kinds of algorithms based on Photoplethysmography signal and respiratory signal about identification are proposed:(?)This paper presents a method of identification based on the segments of the optimal periodic waveform of PPG signal.Firstly,take the amplitude and width of the single period waveform as the selection criteria and the single-period waveforms whose amplitudes and widths within a certain range are chosen as the optimal waveforms.Then the one-cycle waveforms are divided into several parts.The similarity between the waveforms which are in the same segment is calculated as the weight.At the same time,features are extracted by using KPCA method.Using the weighting factors,the weighted fusion of the waveforms are conducted by fusing the feature vectors of every segment.Finally,the fused eigenvectors are predicted to which category by using the SVM classifier.(?)A physiological signal fusion identification method based on the regularized generalized local discriminant canonical correlation analysis is proposed.The method is based on the canonical correlation analysis.By adding a regularization parameters,the noise interference with feature extraction are eliminated,and the category information of the samples are added to the extracted feature by computing the local intra-classcorrelation matrix and the local inter-class correlation matrix.And also the intra-class divergence constraints are computed to make the smaller space between the similar samples and the inter-class divergence constraints are computed to make the larger space between the heterogeneous samples.Then a kind of regularization of generalizes local discriminant canonical correlation analysis algorithm is presented.By using the algorithm,the features of PPG signal and respiration signal are extracted and fused.Finally,the simulation results show the validity of the method by getting the 99.5% rate of identification.