Non-invasive Multi-modal Human Identification System Combining ECG, GSR, and Airflow Biosignals
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A huge amount of data can be collected through a wide variety of sensor technologies. Data mining techniques are often useful for the analysis of gathered data. This paper studies the use of three wearable sensors that monitor the electrocardiogram, airflow, and galvanic skin response of a subject with the purpose of designing an efficient multi-modal human identification system. The proposed system, based on the rotation forest ensemble algorithm, offers a high accuracy (99.6 % true acceptance rate and just 0.1 % false positive rate). For its evaluation, the proposed system was testing against the characteristics commonly demanded in a biometric system, including universality, uniqueness, permanence, and acceptance. Finally, a proof-of-concept implementation of the system is demonstrated on a smartphone and its performance is evaluated in terms of processing speed and power consumption. The identification of a sample is extremely efficient, taking around 200 ms and consuming just a few millijoules. It is thus feasible to use the proposed system on a regular smartphone for user identification.
KeywordsSensor data Bioinformatics Human identification Data mining Ensemble classification
This work was supported by MINECO grant TIN2013- 46469-R (SPINY: Security and Privacy in the Internet of You) and CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks).
- 2.Rostami, M., Juels, A., & Koushanfar, F. (2013). Heart-to-heart (H2H): authentication for implanted medical devices. In Proceedings of the ACM SIGSAC conference on computer & communications security (pp. 1099–1112).Google Scholar
- 3.Rasmussen, K. B., Roeschlin, M., Martinovic, I., & Tsudik, G. (2014). Authentication using pulse-response biometrics. In Proceedings of the network and distributed system security symposium (NDSS).Google Scholar
- 4.Eng, A., & Wahsheh, L. (2013). Look into my eyes: A survey of biometric security. In Proceedings of the tenth international conference on information technology: New generations (pp. 422–427).Google Scholar
- 6.Khalifa, W., Salem, A., Roushdy, M., & Revett, K. (2012). A survey of EEG based user authentication schemes. In Proceedings of the 8th international conference on informatics and systems (pp. BIO-55–BIO-60).Google Scholar
- 7.Spachos, P., Gao, J., & Hatzinakos, D. (2011).Feasibility study of photoplethysmographic signals for biometric identification. In Proceedings of the 17th international conference on digital signal processing (DSP) (pp. 1–5).Google Scholar
- 8.Ichino, M., Sakano, H., & Komatsu, N. (2006). Multimodal biometrics of lip movements and voice using kernel fisher discriminant analysis. In Proceedings of the international conference on control, automation, robotics and vision (pp. 1–6).Google Scholar
- 9.Jani, R., & Agrawal, N. (2013). Proposed framework for enhancing security in fingerprint and finger-vein multimodal biometric recognition. In Proceedings of the international conference on machine intelligence and research advancement (pp. 440–444).Google Scholar
- 10.Revett, K., Deravi, F., & Sirlantzis, K. (2010). Biosignals for user authentication—Towards cognitive biometrics. In Proceedings of the international conference on emerging security technologies (EST) (pp. 71–76).Google Scholar
- 11.Riera, A., Dunne, S., Cester, I., & Ruffini, G. (2008). STARFAST: A wireless wearable EEG/ECG biometric system based on the ENOBIO Sensor. In Proceedings of the international workshop on wearable micro and nanosystems for personalised health (pp. 1–4).Google Scholar
- 16.Clifford, G. D., Azuaje, F., & McSharry, P. (2006). Advanced methods and tools for ECG data analysis. Norwood, MA: Artech House Inc.Google Scholar
- 27.Pal, S., & Mitra, M. (2011). ECG based biometric authentication: a novel data modeling approach. In Proceedings of the international conference on image information processing (ICIIP) (pp. 1–4).Google Scholar
- 29.Shen, T. W., Tompkins, W. J., & Hu, Y. H. (2002). One-lead ECG for identity verification. In Proceedings of the 24th annual conference and the annual fall meeting of the biomedical engineering society EMBS/BMES conference (Vol. 1, pp. 62–63).Google Scholar
- 30.Anderson, C. W., & Bratman, J. A. (2008). Translating thoughts into actions by finding patterns in brainwaves. In Proceedings of the fourteenth Yale workshop on adaptive and learning systems (pp. 1–6).Google Scholar
- 31.Carmona, N., Rua-Seoane, J., Elorza, J., Saenz de Pipaon, E., Palacios, C., & Bragard, J. (2013). Aging of ECG characteristics over a five year period. In Proceedings of the conference on computing in cardiology (CinC) (pp. 1031–1034).Google Scholar
- 32.Schneier, B. (2010). Changing Passwords. https://www.schneier.com/blog/archives/2010/11/changing_passwo.html.
- 34.Tantawi, M. M., Revett, K., Tolba, M. F., & Salem, A. (2012). On the use of the electrocardiogram for biometric authentication. In Proceedings of the 8th international conference on informatics and systems (pp. BIO-48–BIO-54).Google Scholar
- 36.Yoon, C., Kim, D., Jung, W., Kang, C., & Cha, H. (2012). Appscope: Application energy metering framework for android smartphone using kernel activity monitoring. In Proceedings of the USENIX annual technical conference (p. 36).Google Scholar
- 37.Jung, W., Kang, C., Yoon, C., Kim, D., & Cha, H. (2012). DevScope: A nonintrusive and online power analysis tool for smartphone hardware components. In Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis (pp. 353–362).Google Scholar