Total views : 445
Time Variability Analysis of Photoplethysmogram Biometric Identification System
This paper presents the time variability analysis of photoplethysmogram biometric identification system. There have been few researches discussing about the effectiveness of PPG signal as a biometric identification system in different time instances. PPG signals of 5 subjects were obtained from MIMIC II Waveform Database, version 3, part 3 with a sampling frequency of 125 Hz. The signals were pre-process using low pass Butterworth filter. Then, discriminating features were extracted from the PPG waveform in varying time instances (different days). Finally, this PPG samples were classified using commonly known classification techniques for person identification. Based on experimentation results, PPG signals when using LMT and FT gives identification rates of 96% for both classifiers. For sensitivity and specificity test, both LMT and FT give the accuracy of 0.96 and 0.01. The precision test gives the result of 0.962 for LMT and 0.964 for FT. Thus, this outcome suggests the feasibility and robustness of PPG signals as a biometric modality in different time instances.
Biometric, FT, LMT, PPG Signal, Person Identification, Time Variability.
- Jain AK, Arun R, Salil P. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology. 2004; 14(1):4–20.
- Spachos P, Jiexin G, Dimitrios H. Feasibility study of photoplethysmographic signals for biometric identification. IEEE 2011 17th International Conference on Digital Signal Processing (DSP); 2011.
- Zhang GH, Poon CCY, Zhang YT. A biometrics based security solution for encryption and authentication in telehealthcare systems. IEEE 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies ISABEL 2009; 2009.
- Meredith DJ, et al. Photoplethysmographic derivation of respiratory rate: A review of relevant physiology. Journal of Medical Engineering and Technology. 2012; 36(1):1–7.
- Bonissi A, et al. A preliminary study on continuous authentication methods for photoplethysmographic biometrics. Proceedings of the 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS); 2013.
- Singh M, Spiti G. Correlation studies of PPG finger pulse profiles for Biometric system. Int J Inf Technol Knowl Manage. 2012; 5:1–3.
- Salanke NS, Girish R, Maheswari N, Andrews S. An enhanced intrinsic biometric in identifying people by photopleythsmography signal. Proceedings of the 4th International Conference on Signal and Image Processing 2012 (ICSIP 2012); India: Springer; 2013.
- Salanke NS, Girish R, et al. Enhancement in the design of biometric identification system based on photoplethysmography data. 2013 IEEE International Conference on Green High Performance Computing (ICGHPC); 2013.
- Hasan MdR, et al. Single decision tree classifiers’ accuracy on medical data. Proceedings of the 5th International Conference on Computing and Informatics, ICOCI; 2015.
- Sewaiwar P, Kamal KV. Comparative study of various decision tree classification algorithm using WEKA. 2015.
- Sharma TC, Manoj J. WEKA approach for comparative study of classification algorithm. International Journal of Advanced Research in Computer and Communication Engineering. 2013; 2(4):1925–31.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.