anilbas/3DMMasSTN
MatConvNet implementation for incorporating a 3D Morphable Model (3DMM) into a Spatial Transformer Network (STN)
This project helps researchers and developers working with facial image analysis to normalize diverse human faces. It takes a single 2D image of a face and outputs a 'flattened' 2D image where the face is geometrically corrected, removing variations caused by pose changes or self-occlusions. The system learns to fit a 3D morphable model to the face without needing pre-labeled examples. This is ideal for specialists in computer vision, biometrics, and graphics who need a standardized facial representation.
285 stars. No commits in the last 6 months.
Use this if you need to process many 2D facial images and want a consistent, normalized 'texture map' of each face, regardless of their original pose or expression, for further analysis like identity recognition or attribute estimation.
Not ideal if you are looking for a ready-to-use application or a model for general image classification, as this is a framework for advanced facial normalization within a research context.
Stars
285
Forks
50
Language
Matlab
License
Apache-2.0
Category
Last pushed
Apr 15, 2018
Commits (30d)
0
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