Gait Recognition as Non-Intrusive Biometric Using View Invariant Methods in Multi Temporal Images

  • Abstract
  • Keywords
  • References
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  • Abstract

    Gait patterns have been used widely in recent years to authenticate users. Because it doesn’t require user intrusion, it is often used as a biometric to make authentication processes easier and hassle free. But there are various issues with this process. To start with, the viewing angle has to be constant which is quite difficult to achieve with limited number of cameras. Speed too can alter the way a person walks and cause inconsistencies in identification. Furthermore, complications might arise if the subject is carrying something. The weight can affect his walking pattern. Besides, a recent accident could also transform a person’s walking pattern and lead to wrong identification. Other biometrics such as face detection can be combined with this technique to reduce the issues leading to erroneous identification. In this paper, we propose a system to overcome the viewing angle discrepancies. The system takes in walking sequences as input and processes them to create images. This is converted into 3D images by means of stereovision algorithms. Using which, we can effectively match the real-time image with various image sequences in the database. Side face detection can enhance the accuracy further..



  • Keywords

    Background Subtraction, Transformation, Gait Feature Extraction.

  • References

      [1] P. Byuvol, L. Gabsalikhova, I. Makarova, E. Mukhametdinov, G. Sadygova, “Improving the Branded Service Network Efficiency based on its Functioning Evaluation”, Astra Salvensis, Supplement No. 2, p. 373, 2017.

      [2] Azad N., Ghandvar P., Rahimi Z., “Online Search Behaviour of Customers in Shoe Market”, Astra Salvensis, Supplement No. 2, p. 793, 2017.

      [3] Konstantios, Moustakas. (2010). Gait recognition using geometric features and soft biometrics. IEEE SIGNAL PROCESSING. APRIL.

      [4] WorapanKusakunniran. (2013). A New View-Invariant Feature for Cross-View Gait Recognition. IEEE Transactions on Information Forensics and Security. October.

      [5] Liang Wang. (2003). Automatic gait recognition based on statistical shape analysis. IEEE Transactions on Image Processing. September.

      [6] Shiqi Yu. (2009). A study based on gender classification. IEEE Transactions on Image Processing. August.

      [7] ImedBouchrika. et al (2014). Markerless Extraction of Gait Features using Haar-like Template for View-Invariant Biometrics. 15th International Conference on science and techniques of automatic control. December.

      [8] Nitchan Jianwattanapaisarn. (2104). Human Identification using Skeletal Gait and silhouette data extracted by Microsoft Kinect. SCIS &ISIS. December.

      [9] Soharab Hossain Shaikh. et al (2014). Gait Recognition using Partial Silhouette-based approach. 2014 International Conference on Signal processing and integrated networks.

      [10] Yu Guan. (2015). On reducing the effect of covariate factors in gait recognition: A classifier ensemble method. IEEE Transactions on Pattern Analysis. July.

      [11] Ashok Veeraraghavan (2005). Matching Shape Sequences in video with applications in human Movement analysis. IEEE Transactions on Pattern Analysis. December.

      [12] DaigoMuramatsu. (2015). Gait-Based Person Recognition Using Arbitrary View Transformation Model. IEEE TRANSACTIONS ON IMAGE PROCESSING. January.

      [13] WorapanKusakunniran. (2012). Gait Recognition Under Various Viewing Angles Based on Correlated Motion Regression. IEEE Transactions On Circuits And Systems For Video Technology. June.

      [14] Makihara, Y., (2006). Gait recognition using a view transformation model in the frequency domain. Proc. 9th Eur. Conf. Comput. Vis., Graz, Austria, May.

      [15] Bobick, A. F. & Johnson. A. Y. (2001). Gait recognition using static, activity specific parameters. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Vol. 1. Dec, I-423–I-430.

      [16] Yam, C, (2004). Automated person recognition by walking and running via model-based approaches. Pattern Recognition. 37, no. 5, 1057–1072.

      [17] Yang, H.D & Lee, W. (2006). Reconstruction of 3D human body pose for gait recognition. Proc. IAPR Int. Conf. Biometrics. January, 619–625.

      [18] Goffredo, M., (2010). Selfcalibrating view-invariant gait biometrics. IEEE Trans. Syst., Man, Cybern. B, Cybern. 40, no. 4, 997–1008, August.

      [19] Ariyanto, G. & Nixon, M. S. (2012). Marionette mass-spring model for 3D gait biometrics. Proc. 5th IAPR Int. Conf. Biometrics. Mar./Apr. 354–359.

      [20] Urtasun, R. & Fua, P. (2004). 3D tracking for gait characterization and recognition. Proc. 6th IEEE Int. Conf. Autom. Face Gesture Recognition. May. 17–22.

      [21] Kale, A. (2003). Towards a view invariant gait recognition algorithm. Proc. IEEE Conf. Adv. Video Signal Based Surveill., July. 143–150.

      [22] Makihara, Y. (2006). Gait recognition using a view transformation model in the frequency domain. Proc. 9th Eur. Conf. Comput. Vis., Graz, Austria. May. 151–163.

      [23] Kusakunniran, W. (2012). Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans. Circuits Syst. Video Technol. 22, 6, 966–980, June.




Article ID: 20527
DOI: 10.14419/ijet.v7i4.7.20527

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