Biosignal –Based Multimodal Biometric System

  • Kuk Won Ko School of Mechanical and ICT Convergence Engineering, Sun Moon University, Korea
  • Sangjoon Lee School of Mechanical and ICT Convergence Engineering, Sun Moon University, Korea
Keywords: biometric, pattern recognition, per-sonal identification


This study concerns personal identification based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals. We manufactured a bio-signal measurement system that can simultaneously measure ECG and PPG signals, using which three channels of ECG signal and one channel of PPG signal were acquired from the right-hand index finger of a total of 33 subsets for 3 minutes. Lead-I signal of the three-channel ECG signal and the one-channel PPG signal were selected for recognition. For each subject, 160 heartbeats were automatically separated from the acquired bio-signals, and a total of 21 features, comprising 15 ECG features, 4 ECG-related PPG features, and 2 features concerning PPG only, were extracted from each heartbeat. Letting the 21 features form a single data point, heartbeat features of each subset were used as the training data for a support vector machine (SVM) classifier, with the number of data points being adjusted from 10 to 80, and the data points (80 - 150) other than the training data were used as the testing data, in order to investigate the recognition performance indices. As a result, the proposed algorithm showed high recognition performance of 99.28% accuracy, 0.88% FRR, 0.85% FAR, 99.28% sensitivity, and 99.31% specificity, when there are 80 training data points. Moreover, even when there are 10 training data points, the proposed algorithm showed the performance of 92.77% accuracy, 7.23% FRR, 6.29% FAR, 92.77% sensitivity, and 93.21% specificity, which can be evaluated as an extremely high recognition performance considering that there was a total of 4,950 testing data points.


L. Biel, et al., “ECG analysis: A new approach in human identification,” IEEE Transactions on Instrumentation and Measurement, vol. 50, no. 3, pp. 808-812, 2001.

J. M. Irvine, et al., “EigenPulse: Robust human identification from cardiovascular function,” Pattern Recognition, vol. 41, no. 11, pp. 3427-3435, 2008.

T. W. Shen and W.J. Tompkins, “Biometric statistical study of one-lead ECG features and body mass index (BMI),” 27th Annual International Conference Engineering in Medicine and Biology Society, IEEE press, September, 2005.

S. A. Israel, et al., “ECG to identify individuals,” Pattern Recognition, vol. 38, no. 1, pp. 133-142, 2005.

K. N. Plataniotis, D. Hatzinakos, and J. K. M. Lee, “Analysis of human electrocardiogram ECG for biometric recognition,” Proc. of Biometrics Symposiums, Baltimore, USA, 2006.

J. Kim, et al., “An event detection algorithm in ECG with 60Hz interference and baseline wandering,” Proceedings of the 2nd International Conference on Interaction Sciences, Information Technology (ICIS ‘09), Culture and Human New York, NY, USA, 2009.

C. Cortes, and V. Vapnik, “Support-vector network,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.

How to Cite
K. W. Ko and S. Lee, “Biosignal –Based Multimodal Biometric System”, AITI, vol. 2, no. 3, pp. 89-94, Nov. 2016.