Face Recognition Using Transfer learning with Mobile Net and vgg16

MLOps Task 

Face Recognition using Transfer Learning

In this Task we have to create a CNN model using pre trained models and weights like Mobilenet, ImageNet and Vgg16 for face recognition.

In this task we are using the weights from imageNet and Architecture of MobileNet and vgg16

Pre requisite:

1) These libraries should be installed first:

* Keras

* tensorflow

* numpy

* pandas

* opencv-python

Now lets Proceed further:

Step 1:

* First of all we have to collect the dataset on which we are training our model you can use

any images dataset you want or you can open up your webcam and get your face images

Step 2:

* Open Jupyter-notebook and use the following code

this code will open your webcam and take you cropped face images

Step 3:

* Now we are creating the CNN model using mobile net. I am using 5 celebrity datasets

Visit this website:

Download this dataset

https://www.kaggle.com/dansbecker/5-celebrity-faces-dataset

Step 4:

* Use the following code which is uploded on the github 

This will Download ImageNet weights

This will import all the CNN layers and Display the model Summary

This code will do Agumentation of images

This will Load the vgg face model and Train the Model with dataset images

This will open and display the image with the Prediction name

* Now there are 5 celebrity it depends on you how much you want to classify.

In this tutorial i am taking 5 you can change the datasets if you want

Your Folder names should be same as written here . In this Example the folder names of 5 celebrity are n0, n2, n3, n4 in celebrity_dict_n variable.

Step 5: 

That's it After doing all the steps properly your model is ready using Transfer learning with vgg16 face model Architecture trained on image net

By Following the above steps you can also use other Architecture if you want facenet, inception etc.

Github Link for code:

https://github.com/Moiz-Ali-Moomin/MLOps-Task4

moiz.7152@gmail.com

RISE_2020.68.23.5