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Indigodemigod/Face_recognition_from_group_photo

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Inspiration

Dependencies

Install the dependencies first for running the code.

Pre-trained models

Face alignment using MTCNN

How to use

  • First, we need align face data. So, if you need to run the script 'raw_faces_to_aligned_faces.py' first, the face data that is aligned in the 'faces/group_photos' folder will be saved.

  • Secord, we will cluster the same photos together by running the clustering_faces.py and delete the noise manually and combine the clustered faces again using combine_cluster_folder.py

  • Third, we need to create our own classifier with the face data we created. First we will rename the folder name with the name of the person.
    (In the case of me, I had a high recognition rate when I made 30 pictures for each person.) Your own classifier is a ~.pkl file that loads the previously mentioned pre-trained model ('20170511-185253.pb') and embeds the face for each person.All of these can be obtained by running 'making_classifier.py'.

  • Finally, we load our own 'my_classifier.pkl' obtained above and then open the sensor and start recognition by running labeling_faces.py


(Note that, look carefully at the paths of files and folders in all .py)

Result

Directory Strcuture

├── 20170511-185253
│   ├── 20170511-185253.pb 
│   ├── model-20170511-185253.ckpt-80000.data-00000-of-00001
│   ├── model-20170511-185253.ckpt-80000.index
│   └── model-20170511-185253.meta
├── cls
│   └── my_classifier.pkl
├── data
│   ├── det1.npy
│   ├── det2.npy
│   └── det3.npy
├── faces
│   └── aligned photos atomatically generated from raw_photos
├── labelled_faces
│   └── folder cointaining name of that person
├── raw_faces
│   └── Add your group photos here
├── face_recognition
│   ├── __init__.py
│   ├── detect_face.py
│   ├── model_management.py
│   └── facenet.py
├── raw_faces_to_aligned_faces.py
├── making_classifier.py
├── ModelManagement.py
├── labeling_faces.py
├── detect_face.py
├── facenet.py
├── classifying_static_image.py
├── clustering_faces.py
├── combine_cluster_folder.py

Development

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License

Creative Commons License

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