Electrocardiogram (ECG) signal has attracted widespread interest for the potential use in biometrics due to its ease-of-monitoring and individual uniqueness. To address the accuracy and real-time performance problem of human identification, a fast and robust ECG-based identification algorithm was proposed in this study, which was particularly suitable for miniaturized embedded platforms. Firstly, a dynamic-threshold method was used to extract stable ECG waveforms as template samples and test samples; then, based on a modified Dynamic Time Warping (DTW) method, the degree of difference between matching samples was calculated to reach a result of recognition. Considering that ECG is a kind of time-varying and non-stationary signals, ECG template database should be dynamically updated to ensure the consistency of the template and body status and further improve recognition accuracy and robustness. The analysis results with MIT-BIH Arrhythmia database and own experimental data show that the proposed algorithm has an accuracy rate at 98.6%. On the other hand, the average running times of dynamic threshold setting and optimized DTW algorithms on Android mobile terminals are about 59.5 ms and 26.0 ms respectively, which demonstrates a significantly improved real-time performance.