Face detection and recognition into 6 classes of some famous personalities.
Train Data
Gathered 60 images of famous celebrities for training set.
Validation Data
Gathered 18 images of the same celebrities for the validation set.
Using dlib cnn face detector find faces in image and crop faces and store them in separate folders sorting by individual person.
Download 'mmod_human_face_detector' to use as 'dlib cnn face detector'
! wget http://dlib.net/files/mmod_human_face_detector.dat.bz2
!bzip2 -dk mmod_human_face_detector.dat.bz2
The directory structure should look like this -
Directory structure :
|Images /
| |-- (60 images)
|Images_crop /
| |--ayushmaan/
| |--(10 images)
| |--carry/
| |--(10 images)
| |--deep/
| |--(10 imgaes)
| |-- sush/ (10 images)
| |--kriti / (10 images)
| |--modi / (10 images)
|Images_test /
| |-- .. / (18 images)
|Images_test_crop /
| |--ayushmaan / (3 images)
| |--carry / (3 images)
| |--deep / (3 imgaes)
| |--kriti / (3 images)
| |--modi / (3 images)
| |--sush / (3 images)
|Upload_your_images /
| |--Here you can put your own images to test the model
|Your_predictions /
| |--The result
|Face_Recognition.ipynb
|mmod_human_face_detector.dat
Download weights using
! gdown https://drive.google.com/uc?id=1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo
Define vgg-face model architecture and load weights.
Prepare train data and test data from the embeddings and feed into a simple softmax regressor with 3 layers containing first layer with 100 units and tanh activation function , second layer with 10 units and tanh activation function and third layer with 6 units for each person with softmax activation.
Predictions
For an image(may contain multiple faces) extract each face,get embeddings,get prediction from classifier network,make bounding box around face and write person name.
Some Predicted Image Results
