• Biometric identification like fingerprints, retina, palm and voice recognition needs subject’s permission and physical attention.
  • Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission.
  • The purpose of this Project is to detect humans based their Waling styles.
  • We first extract the gait features from image sequences using the Feature Module. Features are then trained based on the frequencies of these feature trajectories, from which recognition is performed.


  • Gait recognition is a promising topic in the biometric technology.
  • The main aim of the technique identifies individuals based on their walk style.
  • In the existing system a Multi-view Gait Generative Adversarial Network (MvGGAN) to generate gait samples to extend gait datasets, which provides adequate gait samples for deep learning-based cross-view gait recognition methods.


  • Less Accuracy
  • Static Recognition
  • Invalid Features generated.


    • Gait recognition is the process where the features of human motion are automatically obtained/extracted and later these features enable us to authenticate the identity of the person in motion.
    • As like other pattern recognition techniques, gait recognition technique also involves 2 stages:
    • Information is derived from human locomotion in the first stage i.e. feature extraction stage.
    • In the next stage, i.e. the recognition stage, a standard similarity computation technique (Incremental Component Analysis) is used to obtain results for being a match or a mismatch.


  • High Accuracy system
  • Complex to system to Hack
  • High security recognition
  • 3D Supports Available


Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements:

  • Hard Disk: Greater than 500 GB
  • RAM: Greater than 4 GB
  • Processor: I3 and Above


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