Yakshagana Character Identification Through Deep Learning with Crown and Facial Makeup Pattern Analysis
Keywords:
character identification, Three tier Convolution Neural Network (CNN), YOLOv5 , Cyclic Gate Recurrent Neural Network (CGRN), Object detection, YakshaganaAbstract
Yakshagana, an intricate theatrical art form originating from Karnataka, encompasses variations such as Thenku-thittu, BadaguThittu, and Badaabadagu Thittu. This research delves into the historical roots, contemporary influence, and evolving makeup trends within Yakshagana. Within the Tenkutittu Yakshagana, diverse crown types take center stage, with the performer's chosen crown and facial makeup pattern serving as key determinants of the portrayed character. Our study focuses on character classes including Vishnu, Devi, Sarpa and Mahisha for character identification. To address the intricate task of character classification in Yakshagana images, this paper employs deep learning methods such as Three Tier CNN and YOLOv5 . Specifically, a Cyclic Gate Recurrent Neural Network is utilized to classify characters like Vishnu, Devi, Sarpa and Mahisha. Following character categorization, the model proceeds to determine disguises. The Three-tier CNN achieves a commendable 90% accuracy in classifying disguises. Through thorough testing, it has been established that YOLOv5, boasting a remarkable 95% accuracy in identifying multiple elements within an image, emerges as the most suitable algorithm for character identification. This research serves as a real-time tool, aiding newcomers in identifying the appropriate crown and makeup pattern for specific Yakshagana figures.
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