: Detailed mesh points to capture "non-manual markers" (facial expressions essential for ASL grammar).
: Normalize all points relative to a "root" point (e.g., the base of the neck or center of the face) to make the features invariant to where the person is standing in the frame.
: Calculate the first and second derivatives of the landmark coordinates to capture the speed and fluidity of the signs.
: For large-scale training pipelines on AWS or Google Cloud. ASL 1000 - Registry of Open Data on AWS
: For easy loading into Python-based models.
: If you are using raw video instead of just landmarks, extract Optical Flow features to track the motion intensity between frames. 4. Data Format for Training
: Detailed mesh points to capture "non-manual markers" (facial expressions essential for ASL grammar).
: Normalize all points relative to a "root" point (e.g., the base of the neck or center of the face) to make the features invariant to where the person is standing in the frame.
: Calculate the first and second derivatives of the landmark coordinates to capture the speed and fluidity of the signs.
: For large-scale training pipelines on AWS or Google Cloud. ASL 1000 - Registry of Open Data on AWS
: For easy loading into Python-based models.
: If you are using raw video instead of just landmarks, extract Optical Flow features to track the motion intensity between frames. 4. Data Format for Training
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