3D Convolution Neural Network based Person Identification using Gait cycles

TL;DR

This paper presents a 3D CNN approach for person identification using gait analysis, focusing on skeletonized gait features to improve accuracy across multiple viewing angles.

Contribution

It introduces a novel method combining skeletonization and 3D CNNs for gait-based person identification, emphasizing lower body features for enhanced accuracy.

Findings

Skeletonized data improves identification accuracy.

Focus on lower body features enhances model performance.

Model evaluated on CASIA B Gait dataset with promising results.

Abstract

Human identification plays a prominent role in terms of security. In modern times security is becoming the key term for an individual or a country, especially for countries which are facing internal or external threats. Gait analysis is interpreted as the systematic study of the locomotive in humans. It can be used to extract the exact walking features of individuals. Walking features depends on biological as well as the physical feature of the object; hence, it is unique to every individual. In this work, gait features are used to identify an individual. The steps involve object detection, background subtraction, silhouettes extraction, skeletonization, and training 3D Convolution Neural Network on these gait features. The model is trained and evaluated on the dataset acquired by CASIA B Gait, which consists of 15000 videos of 124 subjects walking pattern captured from 11 different angles carrying objects such as bag and coat. The proposed method focuses more on the lower body part to extract features such as the angle between knee and thighs, hip angle, angle of contact, and many other features. The experimental results are compared with amongst accuracies of silhouettes as datasets for training and skeletonized image as training data. The results show that extracting the information from skeletonized data yields improved accuracy.