- 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
- Front End – Anaconda IDE
- Backend – SQL
- Language – Python 3.8
- Hard Disk: Greater than 500 GB
- RAM: Greater than 4 GB
- Processor: I3 and Above
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