Smart Security Systems for Real-Time Human Identification Through GAIT Mechanism

Abstract

The rapid evolution of technological landscapes has necessitated more sophisticated security solutions, with gait analysis emerging as a promising biometric identification technique. This innovative approach leverages individuals\' unique walking patterns as a distinctive personal identifier, utilizing advanced computational methods. By integrating Sequential modeling for temporal data processing and MediaPipe Pose for precise pose prediction, the proposed system demonstrates remarkable accuracy and robustness in individual recognition. The methodology capitalizes on the nuanced biomechanical characteristics of human locomotion, transforming walking patterns into a reliable biometric signature. Preliminary research indicates significant potential for future developments, with anticipated enhancements including hybrid biometric integration and multi-person detection capabilities. This approach represents a significant stride in non-invasive, behavioral biometric technologies, offering promising applications across security, surveillance, and personalized authentication domains.

Introduction

Gait, or a person’s walking pattern, is a distinctive biometric trait that enables non-intrusive, contactless identification. Unlike fingerprints or facial recognition, gait can be observed from a distance and is harder to forge. Gait analysis measures parameters like joint angles and walking speed, but traditional systems are often expensive and require specialized equipment. To overcome this, markerless gait recognition uses computer vision and pose estimation (e.g., MediaPipe) to track human motion from video input.

Related Work:

Past research has advanced gait recognition using machine learning, pose estimation, and deep learning (e.g., GaitNet, 3D-CNN, SVM). Recent systems focus on improving accuracy, scalability, and adaptability using hybrid techniques and dynamic temporal modeling. These innovations are shaping future biometric systems for real-world use.

Objective & Purpose:

The goal is to create a secure, accurate, and privacy-preserving gait recognition system for applications in security, healthcare, and surveillance. It avoids using direct biometric identifiers (like faces) and supports real-time, scalable identification while enhancing user privacy.

Proposed System:

A video-based system captures a person walking, extracts key skeletal features (e.g., joint angles, body ratios), and uses a sequential machine learning model to identify individuals. It offers real-time, non-invasive identification while being robust against forgery.

Methodology & Implementation:

1. Data Acquisition: Video capture via cameras, with OpenCV extracting frames.

2. Pre-processing: Cleaning frames and detecting body landmarks using MediaPipe.

3. Feature Extraction: Converting pose data into measurable gait features.

4. Model Training: A neural network classifies individuals using gait signatures.

5. Integration: Outputs predictions from the trained model in a unified system.

Results:

The system accurately identifies individuals under good lighting and within a certain range. Challenges include poor lighting, background noise, and variable walking speeds. Graphs show effective model training with no overfitting, suggesting strong generalization.

Conclusion

The system provides a non-intrusive and precise method for identifying individuals through gait analysis, utilizing the sequential model for processing temporal data and mediapipe pose for extracting skeletal keypoints. It guarantees privacy by refraining from using direct biometric traits, making it appropriate for security, healthcare, and surveillance. But it has some limitations. To build an efficient way, this system needs proper setup of the instruments, and it needs high-end devices. Future improvements include the integration of hybrid biometric technologies, the ability to detect multiple individuals simultaneously, and the development of advanced deep learning models to enhance accuracy, scalability, and real-time performance in various settings.

References

[1] Hii, Chang Soon Tony , Kok Beng Gan, Nasharuddin Zainal, Norlinah Mohamed Ibrahim, Shahrul Azmin, Siti Hajar Mat Desa, Bart van de Warrenburg, and Huay WoonYou. “Automated Gait Analysis Based on a Marker-Free Pose Estimation Model.” Sensors (2023). [2] Amin, Tahir, and Dimitrios Hatzinakos. “Determinants in Human Gait Recognition.” Journal of Information Security (2012). [3] Fathima, S. M. H. Sithi Shameem, R. S. D. Wahida Banu, and S. Mohamed Mansoor Roomi. “Gait Based Human Recognition with Various Classifiers Using Exhaustive Angle Calculations in Model Free Approach.” Circuits and Systems (2016). [4] Zhang, Ziyuan, Luan Tran, Feng Liu, and Xiaoming Liu. “On Learning Disentangled Representations for Gait Recognition.” IEEE Transactions on Pattern Analysis and Machine Intelligence 44.1 (2022). [5] Supraja, P., Tom, R.J., Tiwari, R.S. et al. 3D convolution neural network-based person identification using gait cycles. Evolving Systems 12, 1045–1056 (2021). [6] Kim, J.-W., Choi, J.-Y., Ha, E.-J., and Choi, J.-H. “Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model.” Applied Sciences 13.4 (2023): 2700. [7] S. M. H. Sithi Shameem Fathima1, R. S. D. Wahida Banu, S. Mohamed Mansoor Roomi, “Gait Based Human Recognition with Various Classifiers Using Exhaustive Angle Calculations in Model Free Approach,” 2016 [8] Jong-Wook Kim, Jin-Young Choi, Eun-Ju Ha and Jae-Ho Choi, “Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model,” 2023

Copyright

Copyright © 2025 Prof. Sharanya P S, Himmath Kumar, Naveena , Gaurav B S, Ganesan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.