ABSTRACT

  • 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.

EXISTING SYSTEM

  • 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.

DISADVANTAGES

  • Less Accuracy
  • Static Recognition
  • Invalid Features generated.

PROPOSED SYSTEM

    • 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.

ADVANTAGES

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

PROJECT VIDEO

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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