| --- |
| license: other |
| license_name: lg |
| license_link: LICENSE |
| task_categories: |
| - image-classification |
| language: |
| - en |
| tags: |
| - face |
| - recognition |
| - emotion |
| pretty_name: Micro Facial Expressions |
| --- |
| Micro Facial Expressions Dataset |
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| Dataset ID: LaurenGurgiolo/Micro_Facial_Expressions |
| Task: Facial Emotion Recognition |
| License: Refer to source datasets |
| Languages: N/A |
| Domain: Computer Vision, Affective Computing |
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| Dataset Description |
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| The Micro Facial Expressions dataset is a combined facial emotion recognition dataset composed of two sources: |
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| FER-2013 – a large-scale grayscale facial expression dataset |
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| Micro-Expression Image Dataset – a curated collection of color facial images representing subtle emotional expressions |
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| The dataset is designed to support research in emotion classification, facial expression analysis, and affective computing across varying image resolutions, color spaces, and expression intensities. |
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| Emotion Classes |
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| All images are labeled using the same seven emotion categories: |
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| Label Emotion |
| 0 Angry |
| 1 Disgust |
| 2 Fear |
| 3 Happy |
| 4 Sad |
| 5 Surprise |
| 6 Neutral |
| Dataset Components |
| 1. FER-2013 Dataset |
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| The FER-2013 dataset (Sambare, 2020), originally released on Kaggle, consists of: |
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| Image format: Grayscale |
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| Resolution: 48 × 48 pixels |
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| Preprocessing: |
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| Faces automatically aligned |
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| Centered and normalized to occupy a consistent spatial area |
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| Objective: Emotion classification into one of seven classes |
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| Data splits: |
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| Training set: 28,709 images |
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| Public test set: 3,589 images |
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| This dataset provides a robust baseline for training deep learning models on standardized facial emotion recognition tasks. |
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| 2. Micro-Expression Image Dataset |
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| The micro-expression dataset was compiled using facial images collected from Google Image Search (Irfan, 2022). |
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| Key characteristics: |
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| Image format: Color (RGB) |
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| Resolution: 80 × 80 pixels |
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| Subjects: Children, adults, and elderly individuals |
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| Emotion categories: |
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| Anger |
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| Disgust |
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| Fear |
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| Happiness |
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| Neutral |
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| Sadness |
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| Surprise |
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| Preprocessing Pipeline |
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| A series of Python scripts were used to ensure dataset quality through the following steps: |
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| Removal of duplicate images |
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| Exclusion of images without detectable faces |
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| Cropping of facial regions |
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| Removal of images smaller than 80 × 80 pixels |
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| Resizing all images to 80 × 80 pixels |
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| Final manual inspection and verification |
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| This preprocessing ensures consistency in facial framing and image quality while preserving subtle emotional cues characteristic of micro-expressions. |
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| Intended Use |
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| This dataset is intended for: |
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| Facial emotion recognition research |
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| Micro-expression analysis |
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| Training and evaluation of deep learning models |
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| Cross-dataset generalization studies (grayscale vs. color, macro vs. micro expressions) |
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| Limitations |
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| Images are sourced from publicly available datasets and web searches, which may introduce demographic or cultural biases. |
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| FER-2013 images are low-resolution grayscale, while micro-expression images are higher-resolution color, potentially affecting model generalization. |
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| Citations |
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| Sambare, M. (2020). FER-2013 Facial Expression Recognition Dataset. Kaggle. |
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| Irfan, M. (2022). Micro-Expression Dataset via Google Image Search. |
