Análisis del impacto del reconocimiento facial en el enganche del jugador en videojuegos: un estudio de caso

Abstract

Nowadays, facial recognition—and particularly emotion recognition through facial expressions— has gained importance in various fields, including video games. This research focused on developing an adaptive Flappy Bird-style game prototype aimed at analyzing how automa-tic emotion detection can enhance player engagement by dynamically adapting the gaming experience. The study was conducted in two phases. In the first phase, a non-adaptive pro-totype was created to collect interaction data and facial expressions. A convolutional neural network was trained using the DAiSEE dataset to identify affective states, while DeepFa-ce detected basic emotions. The collected data was compared with the Game Experience Questionnaire (GEQ) to find useful patterns for adaptive design. In the second phase, a new prototype with real-time adaptation mechanisms was built, based on the previously esta-blished relationships. This system adjusted game variables such as speed, difficulty, and visual/auditory stimuli according to the player’s emotional state. Experimental results sho-wed that the adaptive version significantly improved engagement levels compared to the non-adaptive version. These findings demonstrated that integrating emotional recognition techniques with artificial intelligence is an effective approach to enrich human-computer in-teraction and opens new possibilities for designing emotionally responsive video games.