Gait Identification using Neural Network

Biometric System has become more important in security and verification of any human, which is under surveillance. Identification from distance is also possible by this technology. Researchers are taking interest to find out identification of gait by unknown manners and without informing the human as object. We are going to offer sufficient self-similarity gait recognition system for identification using artificial neural network. In which background modeling is made by video camera, in front of camera movement will be generated as to collect frames as segments using background subtraction algorithm. Then logically head (Skelton) is used to find out the walking object as a walking figure. In short, when a video framing is entered, the offered system identifies the gait properties and body based. Offered system is worked with collected gait dataset with different trials. Video framing sequence showed the algorithm attains recognition performance with its accomplishment. Human as Object identification method using gait is a different technique to verify an individual by the way he move or walk and by the intensity of moving on feet. Biometric recognition is method to assess the behavioural properties of anybody by setting up different pattern as according to need. Gait recognition is type of that biometric system which works without giving any hint to moving object quickly. This is the best way of monitoring the people. Using this system different environment can be controlled like airports, banks, airbase to detect the danger and threat.

@article{Talib2017,
  title     = {Gait Identification using Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {9},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Ramzan Talib and Ayesha Shafique and Muhammad Kashif Hanif and Muhammad Umer Sarwar},
  doi       = {10.14569/IJACSA.2017.080909},
  url       = {https://doi.org/10.14569/IJACSA.2017.080909}
}