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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63866
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
CSE
Shree Rawatpura sarkar university raipur
CSE
Shree Rawatpura sarkar university raipur
Abstract: This research paper presents a Hand Gesture Volume Controller system based on Artificial Intelligence and Computer Vision technologies. The system enables users to control the audio volume of a computer through hand gestures captured by a webcam. The project is implemented using Python programming language with OpenCV, MediaPipe, and Pycaw libraries. The main objective of the project is to provide a touchless, efficient, and user-friendly method of volume control. The system detects the distance between fingers and maps it to volume levels in real time. The proposed solution improves user interaction and demonstrates the practical application of gesture recognition systems in modern smart environments.
KEYWORDS
This research successfully demonstrates the implementation of a real-time, touchless Hand Gesture Volume Controller utilizing Google's MediaPipe framework and OpenCV. The experimental outcomes validate that mapping the physical Euclidean distance between 3D hand landmarks offers a highly precise, low-latency, and computationally lightweight alternative to traditional hardware-based input peripherals. By delivering stable performance on standard, built-in consumer webcams without requiring expensive external sensors, the proposed software-driven framework significantly enhances human-computer interaction, improves user accessibility for individuals with physical disabilities, and provides a practical, hygienic solution for modern smart environments.
[1] G. R. S. Murthy and R. S. Jadon, "A Review of Vision Based Hand Gestures Recognition," International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 405-410, 2009. [2] "Real Time Finger Tracking and Contour Detection for Gesture Recognition using OpenCV," IEEE Xplore Digital Library, Conference Proceedings. [3] "Hand Gesture Recognition Techniques for Human Computer Interaction," CiteSeerX Research Platform, Academic Repository. [4] "Hand Gesture Recognition using OpenCV and Python," SpringerLink Academic Press, Lecture Notes in Electrical Engineering. [5] "Volume Control Using Hand Gesture using Python and OpenCV," Hackster.io Developer Portal, Open-Source Hardware and Software Documentation. [6] "Hand Gesture Recognition for Human Computer Interaction," ResearchGate Publications, Technical Review. [7] "Advanced Human Interface via Vision Pipelines," International Journal Research Publisher, Research Article, July 2018. [8] "Volume Control Using Hand Gestures," International Journal of Innovative Science and Research Technology (IJISRT), Volume Publication. [9] "Hand Gesture Recognition System using Camera," International Journal of Engineering Research & Technology (IJERT), Technical Paper.
Publication Date
5/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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