基于门控空间频率融合和集成学习的增强面部情感识别框架

Facial expressions reflect a person's internal emotional state and are interpreted through automated facial emotion recognition (FER) methods in applications such as human-computer interaction, security, and healthcare. Early FER approaches relied on manual feature extraction combined with machine learning (ML) algorithms; however, such methods struggle with large-scale datasets and fail to capture the non-linear characteristics of facial expressions. To address these limitations, this study proposes an enhanced automated facial emotion classification framework based on an ensemble learning ap