Controlling Media Player Through Hand Gesture Recognition System Using CNN and RNN Models

Year : 2024 | Volume :15 | Issue : 01 | Page : 29-34
By

Khallikkunaisa

Mayank N. Nandwani

Rohit Chauhan

Prabhant Kumar

Nitesh Kumar

  1. Associate Professor Computer Science and Engineering, HKBK College of Engineering, Bengaluru Karnataka India
  2. Student Computer Science and Engineering, HKBK College of Engineering, Bengaluru Karnataka India
  3. Student Computer Science and Engineering, HKBK College of Engineering, Bengaluru Karnataka India
  4. Student Computer Science and Engineering, HKBK College of Engineering, Bengaluru Karnataka India
  5. Student Computer Science and Engineering, HKBK College Of Engineering, Bengaluru Karnataka India

Abstract

Artificial intelligence markup language (AIML) project represents a pioneering endeavor in the realm of media player control through hand gesture recognition, merging advanced technologies like convolutional neural networks (CNN) and recurrent neural networks (RNN). By harnessing the image analysis capabilities of CNN, our system ensures accurate, real-time detection, and interpretation of intricate hand gestures, enabling users to interact with their media content naturally and seamlessly. What sets our project apart is the incorporation of RNN, which imbues the system with a temporal understanding of gestures, enhancing its ability to recognize complex sequences of gestures and commands, including actions like play, pause, or nuanced volume adjustments. This synergy of CNN and RNN not only exemplifies the transformative potential of AI and deep learning in human-computer interaction but also promises to redefine the user experience, offering an accessible, responsive, and immersive means of controlling digital entertainment. Our project addresses a critical need for more intuitive user interfaces, particularly for individuals with physical limitations, making media playback more inclusive and engaging.

Keywords: Hand gesture recognition, convolutional neural network (CNN), recurrent neural network (RNN), human-computer interaction, inclusive user interfaces

[This article belongs to Journal of Computer Technology & Applications(jocta)]

How to cite this article: Khallikkunaisa, Mayank N. Nandwani, Rohit Chauhan, Prabhant Kumar, Nitesh Kumar. Controlling Media Player Through Hand Gesture Recognition System Using CNN and RNN Models. Journal of Computer Technology & Applications. 2024; 15(01):29-34.
How to cite this URL: Khallikkunaisa, Mayank N. Nandwani, Rohit Chauhan, Prabhant Kumar, Nitesh Kumar. Controlling Media Player Through Hand Gesture Recognition System Using CNN and RNN Models. Journal of Computer Technology & Applications. 2024; 15(01):29-34. Available from: https://journals.stmjournals.com/jocta/article=2024/view=140190





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Regular Issue Subscription Review Article
Volume 15
Issue 01
Received February 22, 2024
Accepted February 28, 2024
Published April 5, 2024