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FER2013 Cleaned and Extended – mehmet-3emin
This dataset is a cleaned and extended version of the original FER2013 dataset, adapted for facial emotion recognition tasks. It was prepared as part of a senior thesis project at Mersin University in 2025, titled "Videodan Duygusallık Analizi" (Emotion Analysis from Video).
The dataset is formatted for folder-based image classification tasks and includes 7 emotion classes.
🧹 Cleaning Process
The original FER2013 dataset was reviewed and processed using the following steps:
- Removed low-resolution or blurry images (below 48x48 or heavily pixelated)
- Standardized image dimensions and file naming conventions
- Corrected mislabeled samples where clearly identifiable
🧩 Extension Details
To improve generalization and diversity:
- Additional facial images were extracted from various public video sources using OpenCV
- Individual frames containing clearly visible faces were manually cropped
- These new images were manually labeled by human annotators into 7 emotion categories:
angry,disgust,fear,happy,neutral,sad,surprise
This extension allowed the dataset to include more varied lighting, angle, and expression styles, making it more robust for real-world facial emotion detection tasks.
🏷️ Class Distribution
The dataset is organized into folders by emotion class:
fer2013-cleaned/ ├── train/ │ ├── angry/ │ ├── disgust/ │ ├── fear/ │ ├── happy/ │ ├── neutral/ │ ├── sad/ │ └── surprise/ ├── test/ │ └── …
Each folder contains .jpg or .png images with RGB channels and resized to 48x48 or 224x224 resolution.
📦 Usage
from datasets import load_dataset
dataset = load_dataset("mehmet-3emin/fer2013-cleaned")
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