PSewmuthu/Optimized_Video_Facial_Landmarks

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.

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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.

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Data Collection and Processing

Source Video Details (Video Emotion Dataset)

• Source: 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.

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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.

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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.

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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 (Kaggle User: thnhthngchu)

• Extraction Framework: Google Inc. (2020). MediaPipe. https://mediapipe.dev/

• This Dataset:

@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 }
}