Datasets:
Tasks:
Image Classification
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
computer-vision
affective-computing
facial-landmarks
mediapipe
emotion-recognition
feature-extraction
DOI:
License:
File size: 5,815 Bytes
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---
pretty_name: Optimized 478-Point 3D Facial Landmark Dataset
language: en
license:
- apache-2.0
tags:
- computer-vision
- affective-computing
- facial-landmarks
- mediapipe
- emotion-recognition
- feature-extraction
- video-analysis
- optimized
source_datasets:
- thnhthngchu/video-emotion
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
- face-detection
citation:
- "@misc{VideoEmotionDataset,
title={Video Emotion},
author={thnhthngchu},
year={2020},
publisher={Kaggle},
url={https://www.kaggle.com/datasets/thnhthngchu/video-emotion}
}"
- "@misc{MediaPipe,
title={MediaPipe},
author={Google Inc.},
year={2020},
url={https://mediapipe.dev/}
}"
---
# Dataset Card for 478-Point Normalized 3D Facial Landmark Dataset
## Dataset Description
This dataset provides **pre-extracted, normalized 3D facial landmark features** derived from the **Video Emotion** dataset. It is optimized for efficient training of **emotion recognition** and **facial analysis models**, bypassing the need to process large raw video files.
**License:** The extracted feature data in this Parquet file is licensed under **Apache 2.0**. Note that the original source video files may have separate licensing terms.
Each entry (row in the Parquet) represents a single video frame and contains the corresponding emotion label along with 1434 features representing the x, y, z coordinates for 478 distinct facial landmarks, as generated by the MediaPipe Face Landmarker model.
---
## Data Fields and Structure
The data is provided in a single Parquet file, typically named **`emotion_landmark_dataset.parquet`**.
| Column Name | Data Type | Description |
| :--------------- | :----------------- | :---------------------------------------------------------------------------------------------------------------- |
| `video_filename` | String | The identifier of the original video file from which the frame was extracted. |
| `frame_num` | Integer | The sequential frame index within the original video file. |
| `emotion` | String/Categorical | The ground truth emotion label for this **clip**. **Classes include: Angry, Disgust, Fear, Happy, Neutral, Sad.** |
| `x_0` to `x_477` | Float | The normalized X coordinate (horizontal position) for each of the 478 landmarks (0.0 to 1.0). |
| `y_0` to `y_477` | Float | The normalized Y coordinate (vertical position) for each of the 478 landmarks (0.0 to 1.0). |
| `z_0` to `z_477` | Float | The normalized Z coordinate (depth, relative to the face center) for each of the 478 landmarks. |
**Note on Coordinates:** Since the coordinates are **normalized** (0.0 to 1.0), they must be multiplied by the respective pixel width and height of the original frame to visualize them accurately.
---
## Data Collection and Processing
### Source Video Details (Video Emotion Dataset)
- **Source:** [Video Emotion](https://www.kaggle.com/datasets/thnhthngchu/video-emotion) (Kaggle User: thnhthngchu)
- **Domain:** Facial expressions and affective computing, covering a range of scenarios.
- **Labels:** Videos were originally labeled with clip-level emotional categories.
- **License of Original Data:** Users must refer to the licensing terms specified by the original source dataset on Kaggle.
### Feature Extraction Methodology
The features were extracted using the **MediaPipe Face Landmarker** model.
1. **Frame Extraction:** Each video file was processed frame-by-frame.
2. **Landmark Detection:** For each frame, the 478 facial landmarks were detected.
3. **Normalization:** All coordinates (x, y, z) are normalized to the range [0.0, 1.0] relative to the bounding box of the face or the original frame dimensions.
---
## Usage Example and Visualization
To ensure the coordinates have been extracted correctly and to demonstrate the data visually, please refer to the provided **`optimized-3d-facial-landmark-dataset-usage.ipynb`** file in the repository.
This Jupyter Notebook contains a runnable Python example that **loads random video frames**, correctly denormalizes the coordinates using the frame's dimensions, and plots the 478 landmarks on the face.
---
## Potential Applications
- **Transfer Learning:** Use the landmarks as input features for lightweight classifiers (e.g., LSTMs, simple MLPs) for emotion recognition.
- **Biometrics:** Advanced facial tracking and identity verification research.
- **Data Augmentation:** Analyze feature distribution for generating synthetic training data.
---
## Citation
If you use this dataset in your research or project, please use the citation and acknowledge the original source data.
- **Original Data Source:** [Video Emotion](https://www.kaggle.com/datasets/thnhthngchu/video-emotion) (Kaggle User: thnhthngchu)
- **Extraction Framework:** Google Inc. (2020). MediaPipe. <https://mediapipe.dev/>
- **This Dataset:**
```bibtex
@misc{pasindu_sewmuthu_abewickrama_singhe_2025,
author = { Pasindu Sewmuthu Abewickrama Singhe },
title = { Optimized_Video_Facial_Landmarks (Revision 7334b7d) },
year = 2025,
url = { https://huggingface.co/datasets/PSewmuthu/Optimized_Video_Facial_Landmarks },
doi = { 10.57967/hf/6765 },
publisher = { Hugging Face }
}
```
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