Face Detection and Recognition

Detection and recognition of a person in real time live video sequences using FaceNet-Keras and SVM classifier. Currently, working on optimizing the cpu usage.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

OpenCV-Python libraries (CV2)
Keras - Deep learning framework
Numpy library

Working Principle

1. Create a custom dataset of users/people.
   
2. Extract face embeddings from the faces using pretrained keras facenet model in numpy-zip format.
   
3. Train an SVM classifier using these face embeddings in (.npz) format, save the model using pickle.
4. Use this model to predict the face in a video sequence.

Running the Code

1. Create a dataset of the people involved in the "data/" folder. The hierarchy that should be followed is below:
   data/
   train/
   person1/
   image1
   image2
   (...)
   .
   .
   personn/
   val/
   person1/
   (...)
   .
   .
   personn/
   

2. Run the dataset_prep.py program using python3.

   python3 dataset_prep.py
   

3. Run the extract_embeddings.py using python3.

   python3 extract_embeddings.py
   

4. Run the svm_classifier.py.

   python3 svm_classifier.py
   

5. Run webcam.py

   python3 webcam.py
   

Demonstration