I will develop a face recognition app

Closing the Gap to Human-Level Performance in Face Recognition

In modern systems, the conventional pipeline consists of four stages:

1. detect
2. align
3. represent
4. classify

I revisit both the alignment step and the representation step by employing explicit 3D face modeling.

DeepFace shows a very close performance to the human level. Humans have 97.53% score whereas the DeepFace model has 97.35% ± 0.25%. This means that the model can get a higher score than human beings sometimes. I am providing the DeepFace model within Keras for Python in this gig.

This system is a combination of CNN, auto-encoder, and transfer learning.

I trained the model on the largest facial dataset to date (SFC Dataset). The data set has 4.4 million photos of 4030 people. That is exactly the number of nodes in the final layer F8

I tested the developed model with variations of Angelina Jolie and Jennifer Aniston. Surprisingly, the model can verify all instances I fed. You can use this model in real time as well

The changes will be done as per your requirements!