-
-
Notifications
You must be signed in to change notification settings - Fork 529
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
How to get face embedding / encoding? #102
Comments
I may have misunderstood what you are asking, but this project isn't about getting face encodings for recognition purposes. For that, you will need something else, such as https://github.com/ageitgey/face_recognition. |
@MattyB95 Maybe I misunderstood this project, but I thought MTCNN + FaceNet not only allows face detection, but also recognition / identification, by assigning a 128D vector embedding to each face. https://github.com/davidsandberg/facenet Is it correct @ipazc? |
@josephernest I'm not completely familiar with FaceNet but wouldn't that be providing the face recognition/identification encodings. This project would be more about cropping the image to the facial region for that purpose, but I will let @ipazc give his verdict :) |
MTCNN is only used to detect faces in an image, |
@imnimn Is there a Python implementation that packs together: MTCNN to get the face box + facenet or deepface for the encoding? |
@josephernest please check this article: |
…tch processing support - Completely refactored the MTCNN implementation following best coding practices. - Optimized code by removing unnecessary transpositions, resulting in faster computation. Fixes #22. - Transposed convolutional layer weights to eliminate the need for additional transpositions during preprocessing and postprocessing, improving overall efficiency. - Converted preprocessing and postprocessing functions into matrix operations to accelerate computation. Fixes #14, #110. - Added batch processing support to enhance performance for multiple input images. Fixes #9, #71. - Migrated network architecture to TensorFlow >= 2.12 for improved compatibility and performance. Fixes #80, #82, #90, #91, #93, #98, #104, #112, #114, #115, #116. - Extensively documented the project with detailed explanations of thresholds and parameters. Fixes #12, #41, #52, #57, #99, #122, #117. - Added support for selecting computation backends (CPU, GPU, etc.) with the `device` parameter. Fixes #23. - Added new parameters to control the result format (support for x1, y1, x2, y2 instead of x1, y1, width, height) and the ability to return tensors instead of dictionaries. Fixes #72. - Configured PyLint support to ensure code quality and style adherence. - Organized functions into specific modules (`mtcnn.utils.*` and `mtcnn.stages.*`) for better modularity. - Created Jupyter notebooks for visualization and ablation studies of each stage, allowing detailed exploration of layers, weights, and intermediate results. Fixes #88, #102. - Added a comprehensive training guide for the model. Fixes #35, #39. - Updated README with information on the new version, including the complete Read the Docs documentation that describes the process, theoretical background, and usage examples. Fixes #53, #73. - Configured GitHub Actions for continuous integration and delivery (CI/CD). - Fixed memory leak by switching to a more efficient TensorFlow method (`model(tensor)` instead of `model.predict(tensor)`). Fixes #87, #109, #121, #125, #128. - Made TensorFlow an optional dependency to prevent conflicts with user-installed versions. Fixes #95. - Added comprehensive unit tests for increased reliability and coverage.
…tch processing support - Completely refactored the MTCNN implementation following best coding practices. - Optimized code by removing unnecessary transpositions, resulting in faster computation. Fixes #22. - Transposed convolutional layer weights to eliminate the need for additional transpositions during preprocessing and postprocessing, improving overall efficiency. - Converted preprocessing and postprocessing functions into matrix operations to accelerate computation. Fixes #14, #110. - Added batch processing support to enhance performance for multiple input images. Fixes #9, #71. - Migrated network architecture to TensorFlow >= 2.12 for improved compatibility and performance. Fixes #80, #82, #90, #91, #93, #98, #104, #112, #114, #115, #116. - Extensively documented the project with detailed explanations of thresholds and parameters. Fixes #12, #41, #52, #57, #99, #122, #117. - Added support for selecting computation backends (CPU, GPU, etc.) with the `device` parameter. Fixes #23. - Added new parameters to control the result format (support for x1, y1, x2, y2 instead of x1, y1, width, height) and the ability to return tensors instead of dictionaries. Fixes #72. - Configured PyLint support to ensure code quality and style adherence. - Organized functions into specific modules (`mtcnn.utils.*` and `mtcnn.stages.*`) for better modularity. - Created Jupyter notebooks for visualization and ablation studies of each stage, allowing detailed exploration of layers, weights, and intermediate results. Fixes #88, #102. - Added a comprehensive training guide for the model. Fixes #35, #39. - Updated README with information on the new version, including the complete Read the Docs documentation that describes the process, theoretical background, and usage examples. Fixes #53, #73. - Configured GitHub Actions for continuous integration and delivery (CI/CD). - Fixed memory leak by switching to a more efficient TensorFlow method (`model(tensor)` instead of `model.predict(tensor)`). Fixes #87, #109, #121, #125, #128. - Made TensorFlow an optional dependency to prevent conflicts with user-installed versions. Fixes #95. - Added comprehensive unit tests for increased reliability and coverage.
Congrats for this nice project @ipazc!
I see the output of your algorithm is something like:
i.e. it gives the bounding box, the keypoints (nose, mouth, eye, etc.).
But how to get an face embedding / face encoding, to be able to do face identification?
The text was updated successfully, but these errors were encountered: