Controlling Media Player through Hand Gesture Recognition System using CNN & RNN Model

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Year : April 5, 2024 at 3:06 pm | [if 1553 equals=””] Volume :15 [else] Volume :15[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : 01 | Page : –

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    Khallikkunaisa S., Mayank N. Nandwani, Rohit Chauhan, Prabhant Kumar, Nitesh Kumar, Mohammad Aqeel Somsagar

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  1. Associate Professor, Student, Student, Student, Student, Student, Computer Science and Engineering, HKBK College Of Engineering Bengaluru, Computer Science and Engineering, HKBK College Of Engineering, Bengaluru, Computer Science and Engineering, HKBK College Of Engineering, Bengaluru, Computer Science and Engineering, HKBK College Of Engineering, Bengaluru, Computer Science and Engineering, HKBK College Of Engineering, Bengaluru, Computer Science and Engineering, Atria Institute of Technology, Bengaluru, Karnataka, Karnataka, Karnataka, Karnataka, Karnataka, Karnataka, India, India, India, India, India, India

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Abstract

nAIML 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.

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Keywords: Hand Gesture Recognition, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Human-Computer Interaction, Inclusive User Interfaces

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Computer Technology & Applications(jocta)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article: Khallikkunaisa S., Mayank N. Nandwani, Rohit Chauhan, Prabhant Kumar, Nitesh Kumar, Mohammad Aqeel Somsagar Controlling Media Player through Hand Gesture Recognition System using CNN & RNN Model jocta April 5, 2024; 15:-

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How to cite this URL: Khallikkunaisa S., Mayank N. Nandwani, Rohit Chauhan, Prabhant Kumar, Nitesh Kumar, Mohammad Aqeel Somsagar Controlling Media Player through Hand Gesture Recognition System using CNN & RNN Model jocta April 5, 2024 {cited April 5, 2024};15:-. Available from: https://journals.stmjournals.com/jocta/article=April 5, 2024/view=0

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References

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Journal of Computer Technology & Applications

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[if 344 not_equal=””]ISSN: 2229-6964[/if 344]

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Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 01
Received February 22, 2024
Accepted February 28, 2024
Published April 5, 2024

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