Application of ECG signal in identity recognition is studied and is proposed

sparse coding algorithm based on complete dictionary. In preprocessing stage

denoise noises in ECG like baseline wander and interference of heart rate variability. In feature extraction stage

extract single periodic signal to form feature vectors. Utilize the feature vectors to build the dictionary model and then be trained to be redundant dictionary by KSVD. Then every part of feature vectors is used for sparse coding

implement sparse representation in the dictionary. In classification stage

utilize sparse coefficient matrix to build characteristic template vector and use it as characteristic parameters. Then matched output individual identity information through Euclidean distance realize personal identity authentication. Performance test on the proposed algorithm is carried out through two fingers ECG database named CYBHi and surface ECG data and won high recognition rate.