Human Gait Analysis using Gait Energy Image

TL;DR

This paper introduces a gait recognition method using Gait Energy Image (GEI), which simplifies data representation and improves recognition performance compared to traditional template-based methods.

Contribution

The paper proposes a novel gait recognition technique using GEI that consolidates gait cycle information into a single image, reducing storage needs and processing complexity.

Findings

GEI-based recognition outperforms template-based methods.

GEI requires less storage and processing time.

Recognition accuracy improves with GEI despite variations.

Abstract

Gait recognition is one of the most recent emerging techniques of human biometric which can be used for security based purposes having unobtrusive learning method. In comparison with other bio-metrics gait analysis has some special security features. Most of the biometric technique uses sequential template based component analysis for recognition. Comparing with those methods, we proposed a developed technique for gait identification using the feature Gait Energy Image (GEI). GEI representation of gait contains all information of each image in one gait cycle and requires less storage and low processing speed. As only one image is enough to store the necessary information in GEI feature recognition process is very easier than any other feature for gait recognition. Gait recognition has some limitations in recognition process like viewing angle variation, walking speed, clothes, carrying load etc. Our proposed method in the paper compares the recognition performance with template based feature extraction which needs to process each frame in the cycle. We use GEI which gives relatively all information about all the frames in the cycle and results in better performance than other feature of gait analysis.