Comparison Among Physiological Signals for Biometric Identification

  • M. Moreno-Revelo
  • M. Ortega-Adarme
  • D. H. Peluffo-Ordoñez
  • K. C. Alvarez-Uribe
  • M. A. BecerraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10585)


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).


Biometric Classifiers mixture Multimodal system Physiological signals Signal processing 


  1. 1.
    Merone, M., Soda, P., Sansone, M., Sansone, C.: ECG databases for biometric systems. A systematic review. Expert Syst. Appl. 67, 189–202 (2017)CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Stevenage, S., Guest, R.: Combining forces: Data fusion across man and machine for biometric analysis. Image Vis. Comput. 55, 18–21 (2016)CrossRefGoogle Scholar
  4. 4.
    Lourenço, A., Hugo, S., Ana, F.: Unveiling the biometric potential of finger-based ECG signals. Comput. Intell. Neurosci. 2011, 5 (2011)CrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Campisi, P., La Rocca, D.: Brain waves for automatic biometric-based user recognition. IEEE Trans. Inf. Forensics Secur. 9(5), 782–800 (2014)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Beritelli, F., Serrano, S.: Biometric identification based on frequency analysis of cardiac sounds. IEEE Trans. Inf. Forensics Secur. 2(3), 596–604 (2007)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Lee, A., Kim, Y.: Photoplethysmography as a form of biometric authentication. IEEE Sensors, pp. 1–2. IEEE (2015)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Bugdol, M., Mitas, A.: Multimodal biometric system combining ECG and sound signals. Pattern Recogn. Lett. 38, 107–112 (2014)CrossRefGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Liu, Y., Hatzinakos, D.: Earprint: transient evoked otoacoustic emission for biometrics. IEEE Trans. Inf. Forensics Secur. 9(12), 2291–2301 (2014)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Byrne, C.: Signal Processing: A Mathematical Approach. CRC Press, Boca Raton (2014)CrossRefzbMATHGoogle Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    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)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • M. Moreno-Revelo
    • 1
  • M. Ortega-Adarme
    • 1
  • D. H. Peluffo-Ordoñez
    • 2
  • K. C. Alvarez-Uribe
    • 3
  • M. A. Becerra
    • 3
    Email author
  1. 1.Universidad de NariñoPastoColombia
  2. 2.Universidad Técnica del NorteIbarraEcuador
  3. 3.Instituto Tecnológico MetropolitanoMedellínColombia

Personalised recommendations