Abstract
The biometric is an open research field that requires analysis of new techniques to increase its accuracy. Although there are active biometric systems for subject identification, some of them are considered vulnerable to be fake such as a fingerprint, face or palm-print. Different biometric studies based on physiological signals have been carried out. However, these can be regarded as limited. So, it is important to consider that there is a need to perform an analysis among them and determine the effectivity of each one and proposed new multimodal biometric systems. In this work is presented a comparative study of 40 physiological signals from a multimodal analysis. First, a preprocessing and feature extraction was carried out using Hermite coefficients, discrete wavelet transform, and statistical measures of them. Then, feature selection was applied using two selectors based on Rough Set algorithms, and finally, classifiers and a mixture of five classifiers were used for classification. The more relevant results shown an accuracy of 97.7% from 3 distinct EEG signals, and an accuracy of 100% using 40 different physiological signals (32 EEG, and eight peripheral signals).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Merone, M., Soda, P., Sansone, M., Sansone, C.: ECG databases for biometric systems. A systematic review. Expert Syst. Appl. 67, 189–202 (2017)
Komeili, M., Louis, W., Armanfard, N., Hatzinakos, D.: Feature selection for nonstationary data: application to human recognition using medical biometrics. IEEE Trans. Cybern. (99), 1–14 (2017)
Stevenage, S., Guest, R.: Combining forces: Data fusion across man and machine for biometric analysis. Image Vis. Comput. 55, 18–21 (2016)
Lourenço, A., Hugo, S., Ana, F.: Unveiling the biometric potential of finger-based ECG signals. Comput. Intell. Neurosci. 2011, 5 (2011)
Liwen, F., Cai, X., Ma, J.: A dual-biometric-modality identification system based on fingerprint and EEG. In: Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–6. IEEE (2010)
Campisi, P., La Rocca, D.: Brain waves for automatic biometric-based user recognition. IEEE Trans. Inf. Forensics Secur. 9(5), 782–800 (2014)
Tseng, K., Luo, J., Hegarty, R., Wang, W., Haiting, D.: Sparse matrix for ecg identification with two-lead features. Sci. World J. 2015, 1–9 (2015)
Beritelli, F., Serrano, S.: Biometric identification based on frequency analysis of cardiac sounds. IEEE Trans. Inf. Forensics Secur. 2(3), 596–604 (2007)
Wang, J., Wang, C., Chin, Y., Liu, Y., Chen, E., Chang, P.: Spectral-temporal receptive fields and MFCC balanced feature extraction for robust speaker recognition. Multimed. Tools Appl. 76(3), 1–14 (2016)
Lee, A., Kim, Y.: Photoplethysmography as a form of biometric authentication. IEEE Sensors, pp. 1–2. IEEE (2015)
Abo-Zahhad, M., Ahmed, S., Abbas, S.: A new multi-level approach to EEG based human authentication using eye blinking. Pattern Recogn. Lett. 82, 216–225 (2016)
Belgacem, N., Fournier, R., Nait-Ali, A., Bereksi-Reguig, F.: A novel biometric authentication approach using ECG and EMG signals. J. Med. Eng. Technol. 39(4), 226–238 (2015)
Bugdol, M., Mitas, A.: Multimodal biometric system combining ECG and sound signals. Pattern Recogn. Lett. 38, 107–112 (2014)
Dinca, L., Hancke, G.: The fall of one, the rise of many: a survey on multi-biometric fusion methods. IEEE Access 5, 6247–6289 (2017)
Liu, Y., Hatzinakos, D.: Earprint: transient evoked otoacoustic emission for biometrics. IEEE Trans. Inf. Forensics Secur. 9(12), 2291–2301 (2014)
Koelstra, S., Muhl, C., Soleymani, M., Lee, S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)
Byrne, C.: Signal Processing: A Mathematical Approach. CRC Press, Boca Raton (2014)
Xie, X., Wang, S., Juang, S., Lee, S., Lin, K., Wang, X., Deng, N.: An ECG feature extraction with wavelet algorithm for personal healthcare. In: Bioelectronics and Bioinformatics, pp. 128–131 (2015)
Banerjee, S., Mitra, M.: A cross wavelet transform based approach for ECG feature extraction and classification without denoising. In: Control, Instrumentation, Energy and Communication, pp. 162–165 (2014)
Peluffo, D., Rodríguez, J., Castellanos, C.: Metodología para la reconstrucción y extracción de características del complejo QRS basada en el modelo parametrico de Hermite. Semana Técnica de ingenierias eléctrica y electrónica, pp. 1–5 (2008)
Orrego, D., Becerra, M., Delgado, E.: Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection. In: Engineering in Medicine and Biology Society (EMBC), pp. 5282–5285 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Moreno-Revelo, M., Ortega-Adarme, M., Peluffo-Ordoñez, D.H., Alvarez-Uribe, K.C., Becerra, M.A. (2017). Comparison Among Physiological Signals for Biometric Identification. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-68935-7_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68934-0
Online ISBN: 978-3-319-68935-7
eBook Packages: Computer ScienceComputer Science (R0)