Enhancing Avatar Emotion Detection Using Deep Learning with Modified VGG16 Architecture

Emotion detection in avatars plays a crucial role in enhancing user experience and interaction within virtual environments. In this paper, a novel approach utilizing deep learning techniques for avatar emotion detection is presented. A challenging dataset, characterized by its unbalanced nature and limited sample size, reflecting the complexities of real-world scenarios, is utilized. Various deep learning models are customized by adding additional convolutional layers tailored to the specific requirements of avatar emotion detection. This modified architecture of VGG16 achieves an average accuracy of 93.33% across three independent runs, outperforming previous approaches. Addressing the dataset's imbalance, data augmentation techniques, enabling our models to capture nuanced patterns have been employed. Furthermore, this paper discusses the significance of fine-tuning the models using transfer learning, leveraging pre-trained weights to optimize performance and generalizability. The findings of this paper underscore the effectiveness of deep learning in avatar emotion detection with challenging datasets. The high accuracy achieved by the proposed approach showcases its potential for real-world applications, such as virtual reality, gaming, and social platforms, where seamless and accurate avatar emotion representation is paramount. This paper contributes to advancing the field of avatar emotion detection and lays the groundwork for future developments in immersive digital experiences.