Advertisement

Ensemble Learning Based Gender Recognition from Physiological Signals

  • Huiling Zhang
  • Ning Guo
  • Guangyuan Liu
  • Junhao Hu
  • Jiaxiu Zhou
  • Shengzhong Feng
  • Yanjie WeiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10968)

Abstract

Gender recognition based on facial image, body gesture and speech has been widely studied. In this paper, we propose a gender recognition approach based on four different types of physiological signals, namely, electrocardiogram (ECG), electromyogram (EMG), respiratory (RSP) and galvanic skin response (GSR). The core steps of the experiment consist of data collection, feature extraction and feature selection & classification. We developed a wrapper method based on Adaboost and sequential backward selection for feature selection and classification. Through the data analysis of 234 participants, we obtained a recognition accuracy of 91.1% with a subset of 12 features from ECG/EMG/RSP/GSR, 82.3% with 11 features from ECG only, 80.8% with 5 features from RSP only, indicating the effectiveness of the proposed method. The ECG, EMG, RSP, GSR signals are collected from human wrist, face, chest and fingers respectively, hence the method proposed in this paper can be easily applied to wearable devices.

Keywords

Gender recognition Physiological signal Feature selection Ensemble learning Wearable devices 

Notes

Acknowledgement

This work is supported by National Science Foundation of China under grant no. U1435215 and 61433012, Guangdong Provincial Department of Science and Technology under grant No. 2016B090918122, the Science Technology and Innovation Committee of Shenzhen Municipality under grant No. JCYJ20160331190123578, and No. GJHZ20170314154722613, Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund under Grant No. U1501501, and Youth Innovation Promotion Association, CAS to Yanjie Wei.

References

  1. 1.
    Ludwig, S., Oertelt-Prigione, S., Kurmeyer, C., Gross, M., Grüters-Kieslich, A., Regitz-Zagrosek, V., Peters, H.: A successful strategy to integrate sex and gender medicine into a newly developed medical curriculum. J. Women’s Health 24, 996–1005 (2015)CrossRefGoogle Scholar
  2. 2.
    Canevelli, M., Quarata, F., Remiddi, F., Lucchini, F., Lacorte, E., Vanacore, N., Bruno, G., Cesari, M.: Sex and gender differences in the treatment of Alzheimer’s disease: a systematic review of randomized controlled trials. Pharmacol. Res. 115, 218–223 (2017)CrossRefGoogle Scholar
  3. 3.
    Bonetto, M., Korshunov, P., Ramponi, G., Ebrahimi, T.: Privacy in mini-drone based video surveillance. In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Slovenia, pp. 1–6. IEEE (2015)Google Scholar
  4. 4.
    Venetianer, P.L., Lipton, A.J., Chosak, A.J., Frazier, M.F., Haering, N., Myers, G.W., Yin, W., Zhang, Z., Cutting, R.: Video surveillance system employing video primitives. Google Patents (2018)Google Scholar
  5. 5.
    Rukavina, S., Gruss, S., Hoffmann, H., Tan, J.-W., Walter, S., Traue, H.C.: Affective computing and the impact of gender and age. PLoS ONE 11, e0150584 (2016)CrossRefGoogle Scholar
  6. 6.
    Han, H., Otto, C., Liu, X., Jain, A.K.: Demographic estimation from face images: Human vs. machine performance. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1148–1161 (2015)CrossRefGoogle Scholar
  7. 7.
    Bekios-Calfa, J., Buenaposada, J.M., Baumela, L.: Robust gender recognition by exploiting facial attributes dependencies. Pattern Recogn. Lett. 36, 228–234 (2014)CrossRefGoogle Scholar
  8. 8.
    Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, pp. 34–42. IEEE (2015)Google Scholar
  9. 9.
    Dantcheva, A., Brémond, F.: Gender estimation based on smile-dynamics. IEEE Trans. Inf. Forensics Secur. 12, 719–729 (2017)CrossRefGoogle Scholar
  10. 10.
    Pahwa, A., Aggarwal, G.: Speech feature extraction for gender recognition. Int. J. Image Graph. Sig. Process. 8, 17 (2016)CrossRefGoogle Scholar
  11. 11.
    Li, M., Han, K.J., Narayanan, S.: Automatic speaker age and gender recognition using acoustic and prosodic level information fusion. Comput. Speech Lang. 27, 151–167 (2013)CrossRefGoogle Scholar
  12. 12.
    Lu, J., Wang, G., Moulin, P.: Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Trans. Inf. Forensics Secur. 9, 51–61 (2014)CrossRefGoogle Scholar
  13. 13.
    Cao, L., Dikmen, M., Fu, Y., Huang, T.S.: Gender recognition from body. In: Proceedings of the 16th ACM International Conference on Multimedia, Vancouver, Canada, pp. 725–728. ACM (2008)Google Scholar
  14. 14.
    Hu, J.: An approach to EEG-based gender recognition using entropy measurement methods. Knowl.-Based Syst. 140, 134–141 (2018)CrossRefGoogle Scholar
  15. 15.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  16. 16.
    Schapire, R.E., Freund, Y.: Boosting: Foundations and Algorithms. Adaptive Computation and Machine Learning Series. The MIT Press, Cambridge (2012)Google Scholar
  17. 17.
  18. 18.
    Steinberg, D., Colla, P.: CART: classification and regression trees. In: The Top Ten Algorithms in Data Mining, vol. 9, p. 179 (2009)CrossRefGoogle Scholar
  19. 19.
    Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Int. J. Mach. Learn. Technol. 2, 37–63 (2011)CrossRefGoogle Scholar
  20. 20.
    Alfakih, K., Walters, K., Jones, T., Ridgway, J., Hall, A.S., Sivananthan, M.: New gender-specific partition values for ECG criteria of left ventricular hypertrophy: recalibration against cardiac MRI. Hypertension 44, 175–179 (2004)CrossRefGoogle Scholar
  21. 21.
    Romei, M., Mauro, A.L., D’angelo, M., Turconi, A., Bresolin, N., Pedotti, A., Aliverti, A.: Effects of gender and posture on thoraco-abdominal kinematics during quiet breathing in healthy adults. Respir. Physiol. Neurobiol. 172, 184–191 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Huiling Zhang
    • 1
  • Ning Guo
    • 1
  • Guangyuan Liu
    • 2
  • Junhao Hu
    • 3
  • Jiaxiu Zhou
    • 4
  • Shengzhong Feng
    • 1
  • Yanjie Wei
    • 1
    Email author
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  3. 3.Chongqing Optoelectronics Research InstituteChongqingChina
  4. 4.Shenzhen Children’s HospitalShenzhenChina

Personalised recommendations