Advancements in Emotion and Gesture Recognition Using Support Vector Machines (SVM)

Authors: Shewale Sunita Bhiwsan, Suhas Rajaram Mache

Country: India

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Abstract: Advancements in artificial intelligence (AI) and human-computer interaction have propelled the development of emotion and gesture recognition systems. This research explores the integration of Support Vector Machines (SVM) with multimodal data, leveraging facial expressions, body movements, and text sentiment for accurate emotion and gesture classification. By employing techniques such as feature extraction, kernel optimization, and data preprocessing, SVM achieves high performance in binary and multi-class classification tasks. Experimental results highlight the effectiveness of the proposed methodology, achieving 98.7% accuracy on the Digits Dataset and improvements in gesture recognition with refined synthetic datasets. Challenges like cultural nuances, emotion ambiguity, and real-time processing constraints persist, necessitating advancements in feature extraction and neural network integration. Applications span healthcare, security, and virtual assistants, emphasizing privacy and ethical considerations. Future research includes enhanced encryption, real-time systems, and deep learning integration, paving the way for transformative, intuitive human-machine interactions.

Keywords: Emotion Recognition, Gesture Recognition, Support Vector Machine (SVM), Human-Computer Interaction

Paper Id: 231913

Published On: 2024-11-05

Published In: Volume 12, Issue 6, November-December 2024

Cite This: Advancements in Emotion and Gesture Recognition Using Support Vector Machines (SVM) - Shewale Sunita Bhiwsan, Suhas Rajaram Mache - IJIRMPS Volume 12, Issue 6, November-December 2024.

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