CNN-Based Novel Deep Learning Ensemble Model for Kathakali Hand Mudra Recognition and Classification

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SREENA. S; SHIBILI.T; AKHILA. M; LALJI CYRIAC

CNN-Based Novel Deep Learning Ensemble Model for Kathakali Hand Mudra Recognition and Classification

Authors: SREENA. S, SHIBILI.T, AKHILA. M, LALJI CYRIAC

Unique Paper ID: 182988
Volume: 12
Issue: no
PageNo: 86-92

Keywords: Feature extraction Image classification CNN Deep learning

Abstract:
Kathakali, a classical dance form originating from Kerala, India, is characterized by intricate hand gestures (mu- dras), body movements, facial expressions, and background mu- sic. These mudras play a pivotal role in conveying emotions and storytelling, but their complexity often makes them challenging for non-experts to interpret. This paper explores the use of deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) to automatically classify Kathakali hand gestures across five distinct classes. We investigate various methods for data preprocessing and classification, leveraging pre-trained models such as ResNet50, VGG16 and InceptionV3 for feature extraction. Our aim is to enhance the understanding and recognition of Kathakali mudras, making them more accessible to the public and aiding in their preservation.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: no
PageNo: 86-92

CNN-Based Novel Deep Learning Ensemble Model for Kathakali Hand Mudra Recognition and Classification

Available:https://ijirt.org/article?manuscript=182988

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