/Face_Recognision

Face Recognition with VGG and Resnet model by using Transfer learning.

Primary LanguagePython

Face Recognision.

Face Recognision with VGG and Resnet model by using Transfer learning.

Requirements

Files & Folders

  • faces - Faces of persons kept inside each Folder named with person's Name.
  • Images - Images of person Kept inside Directory Named with person's Name.
  • video_frames - Frames of person kept inside folder with persons name if video is used to collect Images.
  • face_extrator.py - To seperate faces from photos.
  • frame_capture.py - Use to seperate frames from individual videos.
  • live_input.py - To test final model after training which take input from Webcam and recognise the persons in video
  • model_train.py - To Train Model after seperating each persons faces and keeping them in Correct Folders.

Implementation

  • Input from video

    • Place the persons video in Main directory and start frame_capture.py file on each Video
    • Code - '''Shell python frame_capture.py name_1.mp4 '''
    • This will place frames from each video in video_frames folders inside name_1 folder.

  • Faces from Frames

    • You can take input from Video or collect individual photos of each person and keep inside images folder
    • Code - '''Shell python face_extractor.py video_frames/name_1 or python face_extractor.py images/name_1 '''
    • This will extract faces from photos and place them faces folder inside there corresponding Folders.

  • Start Training -

    • Before starting Make sure the Directories are formed in correct order.
    • faces Folder have structure -> faces

    ----> name_1

    --------> file_of_name_1

    --------> file_of_name_1

    --------> file_of_name_1

    --------> file_of_name_1

    ----> name_2

    --------> file_of_name_2

    --------> file_of_name_2

    --------> file_of_name_2

    --------> file_of_name_2

    ----> name_3

    --------> file_of_name_3

    --------> file_of_name_3

    --------> file_of_name_3

    --------> file_of_name_3

    ...

    • Start the training with Command " python model_train.py resnet " or " python model_train.py vgg " to train model with either Resnet50 or VGG16 Model . VGG16 is smaller model and trains faster and requires less RAM Compare to Resnet50 model.
    • If everything is successful you will find named Model_VGGFace.h5 inside main directory.

  • Test Model -

    • Start the video input from Webcam by command " python live_input.py "
    • If everything is Successful you will find Rectangle around faces with name of person written over it.

Note

  • You can change values inside file face_extractor.py file and live_input.py file and play with scaleFactor , minNeighbors of HaarCascade .These values work fine for me.
  • Some Manual Assist may require after face_extration as some wrong files may come along with faces.

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