Deteksi Ekspresi Wajah Pada Scene Film Menggunakan Residual Masking Network - Bhanu Sri Nugraha; Fahma Inti Ilmawati; Dhani Ariatmanto; Lukman (The Indonesian Journal of Computer Science, 2025)
In a film, there are various things that must be paid attention to, one of which is the actor's expression in deepening his role. This is what can make the audience immersed in the storyline of the film and can provide value for the accuracy of deepening the role for the people who watch it. With the popularity of deep learning, especially CNN (Convolutional Neural Network) can automatically extract and learn for a good facial expression recognition system. In this experiment, we use Residual Masking Network (RNM). Building on this understanding, we evaluate this dataset with standard image classification models to analyse the feasibility of using facial expressions in determining the appropriateness of emotional content in an actor's role in a film. The accuracy results in this study were 99% for detecting angry expressions.