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Gait Analysis for Gender Classification in Forensics

  • Paola BarraEmail author
  • Carmen Bisogni
  • Michele Nappi
  • David Freire-Obregón
  • Modesto Castrillón-Santana
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1123)

Abstract

Gender Classification (GC) is a natural ability that belongs to the human beings. Recent improvements in computer vision provide the possibility to extract information for different classification/recognition purposes. Gender is a soft biometrics useful in video surveillance, especially in uncontrolled contexts such as low-light environments, with arbitrary poses, facial expressions, occlusions and motion blur. In this work we present a methodology for the construction of a gait analyzer. The methodology is divided into three major steps: (1) data extraction, where body keypoints are extracted from video sequences; (2) feature creation, where body features are constructed using body keypoints; and (3) classifier selection when such data are used to train four different classifiers in order to determine the one that best performs. The results are analyzed on the dataset Gotcha, characterized by user and camera either in motion.

Keywords

Gender Classification Gait analysis Supervised learning SVC Random forest AdaBoost KNN 

References

  1. 1.
    Barra, P., Bisogni, C., Nappi, M., Freire-Obregón, D., Castrillon-Santana, M.: Gender classification on 2D human skeleton, pp. 1–4 (2019).  https://doi.org/10.1109/BIOSMART.2019.8734198
  2. 2.
    Connor, P., Ross, A.: Biometric recognition by gait: a survey of modalities and features. Comput. Vis. Image Underst. 167, 1–27 (2018).  https://doi.org/10.1016/j.cviu.2018.01.007CrossRefGoogle Scholar
  3. 3.
    Neves, J., Narducci, F., Barra, S., et al.: Biometric recognition in surveillance scenarios: a survey. Artif. Intell. Rev. 46, 515–541 (2016).  https://doi.org/10.1007/s10462-016-9474-xCrossRefGoogle Scholar
  4. 4.
    Choudhary, S., Prakash, C., Kumar, R.: A hybrid approach for gait based gender classification using GEI and spatio temporal parameters. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, 2017, pp. 1767–1771.  https://doi.org/10.1109/ICACCI.2017.8126100
  5. 5.
    Shakhnarovich, G., Viola, P., Moghaddam, B.: A unified learning framework for real time face detection and classification. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 16–19 (2002)Google Scholar
  6. 6.
    Leng, X., Wang, Y.: Improving generalization for gender classification. In: International Conference on Image Processing, pp. 1656–1659 (2008)Google Scholar
  7. 7.
    Moghaddam, B., Yang, M.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24, 707–711 (2002)CrossRefGoogle Scholar
  8. 8.
    Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. arXiv preprint arXiv:1812.08008 (2018)
  9. 9.
    Gao, W., Ai, H.: Face gender classification on consumer images in a multiethnic environment. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 169–178. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01793-3_18CrossRefGoogle Scholar
  10. 10.
    Guo, G.D., Dyer, C., Fu, Y., Huang, T.S.: Is gender recognition affected by age? In: IEEE International Workshop on Human-Computer Interaction (HCI 2009), in Conjunction with ICCV 2009 (2009)Google Scholar
  11. 11.
    Wang, Y., Ricanek, K., Chen, C., Chang, Y.: Gender classification from infants to seniors. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–6 (2010)Google Scholar
  12. 12.
    Zheng, S, Zhang, J., Huang, K., He, R., Tan, T.: Robust view transformation model for gait recognition. In: Proceedings of the IEEE International Conference on Image Processing (2011)Google Scholar
  13. 13.
    Cavallaro, A., Brutti, A.: Audio-visual learning for body-worn cameras. In: Computer Vision and Pattern Recognition, pp. 103–119 (2019)Google Scholar
  14. 14.
    Divate, C.P., Ali, S.Z.: Study of different bio-metric based gender classification systems. In: International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE (2018).  https://doi.org/10.1109/ICIRCA.2018.8597340
  15. 15.
    Ngan, M.L., Grother, P.J.: Face Recognition Vendor Test (FRVT): Performance of Automated Gender Classification Algorithms (2015).  https://doi.org/10.6028/NIST.IR.8052
  16. 16.
    Castrilln-Santana, M., Lorenzo-Navarro, J., Ramn-Balmaseda, E.: Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild. J. Image Vis. Comput. 57(C), 15–24 (2017).  https://doi.org/10.1016/j.imavis.2016.10.004CrossRefGoogle Scholar
  17. 17.
    Wu, Q., Guo, G.: Gender recognition from unconstrained and articulated human body. Sci. World J. 2014, 12 (2014).  https://doi.org/10.1155/2014/513240. Article ID 513240CrossRefGoogle Scholar
  18. 18.
    Dago-Casas, P., González-Jiménez, D., Yu, L.L., Alba-Castro, J.L.: Single- and cross- database benchmarks for gender classification under unconstrained settings. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, pp. 2152–2159 (2011).  https://doi.org/10.1109/ICCVW.2011.6130514
  19. 19.
    Cao, L., Dikmen, M., Fu, Y., Huang, T.S: Gender recognition from body. In: Proceedings of the 16th ACM International Conference on Multimedia, MM 2008, pp. 725–728 (2008).  https://doi.org/10.1145/1459359.1459470
  20. 20.
    Shelke, P.B., Deshmukh, P.R.: Gait based gender identification approach. In: 2015 Fifth International Conference on Advanced Computing & Communication Technologies. IEEE (2015).  https://doi.org/10.1109/ACCT.2015.66
  21. 21.
    Sabir, A., Al-Jawad, N., Jassim, S., Al-Talabani, A.: Human gait gender classification based on fusing spatio-temporal and wavelet statistical features. In: 2013 5th Computer Science and Electronic Engineering Conference (CEEC). IEEE (2013).  https://doi.org/10.1109/CEEC.2013.6659461
  22. 22.
    Isaac, E.R.H.P., Elias, S., Rajagopalan, S., Easwarakumar, K.S.: Multiview gait-based gender classification through pose-based voting. Pattern Recogn. Lett. 126, 41–50 (2018).  https://doi.org/10.1016/j.patrec.2018.04.020
  23. 23.
    Hassan, O.M.S., Abdulazeez, A.M., Tiryaki, V.M: Gait-based human gender classification using lifting 5/3 wavelet and principal component analysis. In: 2018 International Conference on Advanced Science and Engineering (ICOASE). IEEE (2018).  https://doi.org/10.1109/ICOASE.2018.8548909
  24. 24.
    Jain, A., Kanhangad, V.: Gender classification in smartphones using gait information. Expert Syst. Appl. 93, 257–266 (2018).  https://doi.org/10.1016/j.eswa.2017.10.017CrossRefGoogle Scholar
  25. 25.
    Liu, T., Ye, X., Sun, B.: Combining convolutional neural network and support vector machine for gait-based gender recognition. In: 2018 Chinese Automation Congress (CAC). IEEE (2018).  https://doi.org/10.1109/CAC.2018.8623118
  26. 26.
    Friedman, J.H., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337–407 (2000).  https://doi.org/10.1214/aos/1016218223MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10(4), 1–14 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Paola Barra
    • 1
    Email author
  • Carmen Bisogni
    • 1
  • Michele Nappi
    • 1
  • David Freire-Obregón
    • 2
  • Modesto Castrillón-Santana
    • 2
  1. 1.University of Salerno, Dipartimento di InformaticaFiscianoItaly
  2. 2.Universidad de Las Palmas de Gran Canaria (ULPGC)Las Palmas, Gran CanariaSpain

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