Project Name: Face Match

This project use a deeplearning model like VGGFace and to build a web application Streamlit is used.

Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. Nevertheless, it is remained a challenging computer vision problem for decades until recently.

Deep learning methods are able to leverage very large datasets of faces and learn rich and compact representations of faces, allowing modern models to first perform as-well and later to outperform the face recognition capabilities of humans.

In this project, you will discover the problem of face recognition and how deep learning methods can achieve superhuman performance to identify similar faces.

Demo Video:

This is a methods of identifying similar faces check various aspects on pictures, including: face shape, nose, eyes and mouth; face position in the picture; skin color (including the lighting of the photo); color and hair and cosine_similarity.

Dataset

Dataset for this project is used from

Model Used to Predict

Here I used VGGFace model to extract the facial features.

Webapp Building

Here Streamlit is used to develop the web application and deployed in a local server.

Some Real Time Demo:

Web app look

Lets check some of images

STEPS to run this project:

STEP 01:

Clone the repository

git clone https://github.com/dipesg/face_match.git

STEP 02:

Create an environment

conda create -n celebrity python=3.7 -y

STEP 03:

Install the requirements

pip install -r requirements.txt

STEP 04:

Download the data from the link and keep it in your project directory. Make sure all the actors and actress folder should be in just one folder called data.

STEP 05:

Just execute this command one time if you are not changing the data

python run.py

STEP 06:

Now to start the webapp run the following command

streamlit run app.py