Human Authentication Based on ECG Waves Using Radon Transform
Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like electrocardiogram (ECG) of a person is unique and secure. In this paper, we propose a human authentication system based on ECG waves considering a plotted ECG wave signal as an image. The Radon Transform is applied on the preprocessed ECG image to get a radon image consisting of projections for θ varying from 0 o to 180 o . The pairwise distance between the columns of Radon image is computed to get a feature vector. Correlation Coefficient between feature vector stored in the database and that of input image is computed to check the authenticity of a person. Then the confusion matrix is generated to find False Acceptance Ratio (FAR) and False Rejection Ratio (FRR). This methodology of authentication is tested on ECG wave data set of 105 individuals taken from Physionet QT Database. The proposed authentication system is found to have FAR of about 3.19% and FRR of about 0.128%. The overall accuracy of the system is found to be 99.85%.
KeywordsFAR FRR Pairwise Distance Pre-processing Radon Transform
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- 1.Jain, A.K., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Trans. on Circuits Sys. 14(1), 4–20 (2004)Google Scholar
- 2.Hegde, C., Manu, S., Deepa Shenoy, P., Venugopal, K.R., Patnaik, L.M.: Secure Authentication using Image Processing and Visual Cryptography for Banking Applications. In: Proc. Int. Conf. on Advanced Computing (ADCOM-2008), pp. 65–72 (December 2008)Google Scholar
- 3.Hegde, C., Srinath, U.S., Aravind Kumar, R., Rashmi, D.R., Sathish, S., Deepa Shenoy, P., Venugopal, K.R., Patnaik, L.M.: Ear Pattern Recognition using Centroids and Cross-Points for Robust Authentication. In: Proc. Second Int. Conf. on Intelligent Human and Computer Interaction (IHCI 2010), pp. 378–384 (2010)Google Scholar
- 4.Hegde, C., Rahul Prabhu, H., Sagar, D.S., Vishnu Prasad, K., Deepa Shenoy, P., Venugopal, K.R., Patnaik, L.M.: Authentication of Damaged Hand Vein Patterns by Modularization. In: Proc. of IEEE Region Ten Conference, TENCON 2009 (2009)Google Scholar
- 8.Esbensen, K., Schonkopf, S., Midtgaard, T.: Multivarate Analysis in Practice, 1st edn., vol. 1 (1997)Google Scholar
- 9.Shen, T.W., Tompkins, W.J., Hu, Y.H.: One-Lead ECG for Identity Verification. In: Proc. of Second Joint Conf. of IEEE EMBS/BMES, pp. 62–63 (2002)Google Scholar
- 11.Swamy, P., Jayaraman, S., Girish Chandra, M.: An Improved Method for Digital Time Series Signal Generation from Scanned ECG Records. In: Int. Conf. on Bioinformatics and Biomedical Technology (ICBBT), pp. 400–403 (2010)Google Scholar
- 12.Jose, C.R.S., Fred, A.L.N.: A Biometric Identification System based on Thyroid Tissue Echo-Morphology. In: Int. Joint Conf. on Biomedical Engineering Systems and Technologies, pp. 186–193 (2009)Google Scholar
- 13.Chen, B., Chandran, V.: Biometric Based Cryptographic Key Generation from Faces. In: Proc. of the 9th Biennial Conf. of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp. 394–401 (2007)Google Scholar
- 15.Ariyapreechakul, P., Covavisaruch, N.: Personal Verification and Identification via Iris Pattern using Radon Transform. In: Proc. of First National Conf. on Computing and Information Technology, pp. 287–292 (2005)Google Scholar
- 16.Laguna, P., Mark, R.G., Goldberger, A.L., Moody, G.B.: A Database for Evaluation of Algorithms for Measurement of QT and Other Waveform Intervals in the ECG. Computers in Cardiology, 673–676 (1997)Google Scholar