Leveraging Convolutional Neural Networks for Evaluating Horror Games: A Novel Approach for Capturing Player Experience and Feedback - Gutti Venkata Subbrao; Dr. k. Venkata Raju (Utilitas Mathematica, 2025)
Horror games stand out as one of the most viscerally immersive forms of content in the rapidly evolving space of interactive entertainment. Understanding and improving the horror game player experience therefore requires a methodical approach and employs metrics that can characterise the harnessing of various emotions and perturbations elicited by these titles. Traditional assessment approaches often rely on qualitative measures that are often highly subjective and hard to quantify. In this paper, we present a new method to assess horror games using real-time player reaction analysis with convolutional neural networks (CNNs). We introduce a model that utilises Convolutional Neural Networks (CNNs) to analyse in-game behaviour, physiological data and facial expressions to quantify experience with minimal bias. We train our model on biometric data derived from instances of gameplay sessions and from offline video-clips of people’s faces as they play games. By correlating these metrics with specific in-game events captured by game engines, our technology can detect trends and predict levels of fright, worrying or engagement. We found that CNNs perform with high accuracy at recognising and categorising emotions, offering an instrument to improve the work of horror game developers. Moreover, our approach can be scaled to improve the player experience for other gaming genres with similar data-driven recommendations. This research will ultimately enable better, more sophisticated and more flexible game production methods for the creation of games that will draw us closer to fictional experiences and are more remarkable than many of the games that are currently available.