Human Identification by Gait Using Time Delay Neural Networks


  •  Eman mashagba    

Abstract

This paper proposed human identification method by gait. Human gait is a type of biometric features and related to the physiological and behavioral features of a human. In this paper, a feature vector of gait motion parameters is extracted from each frame using image segmentation methods, and categorized into different categories. Two of these categories were used to form the gait motion trajectories; Category one: Gait angle velocity: angle velocity hip, angle velocity knee, angle velocity thigh and angle velocity shank. Category two: Gait angle acceleration: angle acceleration hip, angle acceleration knee, angle acceleration thigh and angle acceleration shank for each image sequence. Finally, the TDNN method with different training algorithms is used for recognition purpose. This experiment is done on our own database. This research  developed a method which  achieves a higher  recognition rate in the training set 100% and in the testing set 83%. Also, category one  establishes gait motion features to be used in human gait identification applications using different training algorithms, While category two achieved a higher recognition rate by trainrb algorithm.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

WJCI (2020): 0.439

Impact Factor 2020 (by WJCI): 0.247

Google Scholar Citations (March 2022): 6907

Google-based Impact Factor (2021): 0.68

h-index (December 2021): 37

i10-index (December 2021): 172

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