FER2025: Explainable Gender-Aware Facial Emotion Detection via Hybrid Deep Learning and Customized Visual Dataset

This dataset was created for a deep learning project on facial emotion recognition with gender classification. It contains 7,386 images and 11,253 annotations sourced from copyright-free platforms, ensuring legal and unrestricted use. The dataset includes 12 classes representing six emotions (happy, sad, angry, fear, surprised, sleepy), each labeled for male and female subjects. It is split into training (70%), validation (20%), and test (10%) sets. The metadata_FER2025.csv file provides structured details for each image, including set type, class information, and bounding box coordinates, enabling efficient object detection model training.