About: It is one of the most popular and widely used facial recognition models and recorded 98.78% accuracy for the Labeled Faces in the Wild dataset. It also had 68.17% accuracy on the masked-faced dataset called RMFRD.
Steps to follow in the project :
Step 1: Dataset acquisition
Step 2: Detect faces.(will be using dlib)
Step 3: Download and load VGG_face weights.
Step 4: Get embeddings for faces.
Step 5: Train Softmax regressor for 6 person classification from embeddings.
DARK-art108
commented
3 years ago
About: It is one of the most popular and widely used facial recognition models and recorded 98.78% accuracy for the Labeled Faces in the Wild dataset. It also had 68.17% accuracy on the masked-faced dataset called RMFRD.
Steps to follow in the project :
Step 1: Dataset acquisition Step 2: Detect faces.(will be using dlib) Step 3: Download and load VGG_face weights. Step 4: Get embeddings for faces. Step 5: Train Softmax regressor for 6 person classification from embeddings.
I'd Like to work on this as part of the GSSOC-21