Human identification based on biological features is one of the research hotspots nowadays. Considering electrocardio signals are relatively stable and they can be easily acquired, identification using electrocardio signals attracts the attention of many researchers. Traditional identification methods which are based on electrocardio signals usually extract the features artificially. The procedures are complicated and could be easily affected by noise. Because QRS complex is stable even though the duration of cardiac cycle changes, this research uses QRS complex to identify humans. The electrocardio signals are denoised by the wavelet threshold denoising method, and the QRS complexes are extracted to be transferred to binary images. These images are feed to convolutional neural networks to do the identification. This paper compares the performance of several neural networks of different hyperparameters, and finds the highest accuracy reaches 982%. Besides, this paper discusses some other human identification methods which are based on electrocardio signals. Results show that the method proposed in this paper is better than the others.