Emotionalyzer: Player’s Facial Emotion Recognition ML Model for Video Game Testing Automation

In video game development, the play testing phase is crucial for evaluating and optimizing user perception before launch. These tests are often costly and require significant time investment, as they are conducted by experts observing gameplay sessions, which makes capturing real-time data, such as facial and bodily expressions, challenging. Additionally, many independent studies lack the necessary resources to conduct professional testing. Therefore, smaller developers need more cost-effective and time-efficient alternatives to improve their products and streamline the development process. This project aims to develop a real-time facial emotion recognition model using machine learning, which will be integrated into an application that records the player’s emotions during the gameplay session. It seeks to benefit Peruvian indie companies by reducing costs and time associated with traditional testing and providing a more precise and detailed evaluation of the user experience. Additionally, the use of machine learning technology ensures continuous adaptation and progressive improvements in the model over time.