Real-Time Multi-Mode Hand Gesture Recognition Using   MediaPipe and Deep Learning for Human-Computer Interaction

Anil Khatak, Sheenam Naaz

Real-Time Multi-Mode Hand Gesture Recognition Using MediaPipe and Deep Learning for Human-Computer Interaction

Authors

Anil Khatak, Sheenam Naaz

Keywords:

AI, Gesture Recognition ·Media Pipe ·Human Computer Interaction (HCI) ·Deep learning

Abstract

This research presents a real-time multi-mode hand gesture recognition system designed to enabletouchless human-computer interaction using only a standard webcam. The proposed system integratesGoogle’s MediaPipe Hands framework for efficient hand landmark detection and a lightweight neural network classifier developed with TensorFlow for gesture recognition

References

Arora, S., Khandelwal, R.: Gesture-controlled media player using deep learning and opencv. International Journal of Information Technology 15, 341–350 (2023)

Deshmukh, K., Salunke, S.: Real-time implementation of gesture-controlled system using mediapipe. In: IEEE International Conference on Emerging Smart Computing and Informatics (ESCI). pp. 386–390 (2023)

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Published

2024-11-20

How to Cite

Anil Khatak, Sheenam Naaz. (2024). Real-Time Multi-Mode Hand Gesture Recognition Using MediaPipe and Deep Learning for Human-Computer Interaction . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6610–6621. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3848