Evaluating Actors' Performance Using Machine Learning for Gesture Classification
Hossam Ahmed, Omar Amr, Ayman Atia · 2024
In the context of advancing technologies in the entertainment industry, this study explores the utilization of machine learning (ML) for assessing actors' performances. The integration of technology in that industry is limited. The methodology encompasses acquiring performance ratings from the model to recognize predefined criteria. These ratings are then subjected to comparative analysis to gauge the system's effectiveness. The system showed 83.2% accuracy using MediaPipe to extract the positions of body part points for each gesture. Then, using One Dollar Py library, positive, mediocre, and negative models are developed using the patterns extracted from MediaPipe. We benchmarked the system against an assessment by a professional director. This work suggests a pathway to the use of Machine Learning tools in the industry and could be viewed as an innovative method for automating performance assessment with real-world applications that extend across the film-making process.