Facial Recognition

Overview

A Facial Recognition pipeline using a pretrained Tensorflow implementation of Facenet. This pipeline uses a Harr Cascade Classifier to first detect all the faces in a scene. It then runs each of the faces through Facenet which computes a 128 dimensional feature map for each face. Then an SVC (Support Vector Classifier) is used to predict a probability of the face belonging to each class. The class with the highest prediction confidence that is also at least 80% is determined to be the correct classification. If none of the predictions achieve 80% confidence then the face is classified as "Unknown".

This project was made durring HackUMass 2019 as part of a security system my team and I were developing. This was my contribution.

Installation

Git clone and cd into the directory

git clone https://github.com/greerviau/Facial-Recognition.git && cd Facial-Recognition

Download the pretrained weights here and extract them to facenet/src/models/pretrained_model

Usage

First you have to collect images of the faces you want to detect.

Run: collect_photos.py <name-of-subject> <number-of-photos> and use it to take pictures of a subjects face.
Collect photos with different facial expressions and angles.
Repeat this for all the subjects whos faces you want to detect.

Next the images need to be aligned. Run:

python3 facenet/src/align_dataset_mtcnn.py "path/to/faces" "path/to/aligned_faces" --image_size 160

path/to/faces = path to the main folder where you saved your folder seperated faces
path/to/aligned_faces = path to the folder to save your aligned faces

Once your images are aligned you need to train the SVC (Support Vector Classifer) on your dataset. Run:

python3 facenet/src/classifier.py TRAIN "path/to/aligned_faces" "facenet/src/models/pretrained_model" "facenet/src/sv_classifier.pkl"

Finally run test.py to test that it is working.

Pipeline

To use this as a pipeline you will need to import facial_recognition.py The facial recognition can be used by creating a FacialRecognition object an calling find_faces() as shown bellow.

import cv2
from facial_recognition import FacialRecognition

fr = FacialRecognition()

image = cv2.imread('path/to/image')

image, names, faces_frame = fr.find_faces(image, return_face_frame = True)

if faces_frame is not None:
	cv2.imshow('Faces', faces_frame)
cv2.imshow('Facial Recognition',frame)

cv2.waitKey(0)

cv2.destroyAllWindows()

References

Pretrained Facenet model https://github.com/davidsandberg/facenet
Implementing Facenet https://medium.com/@athul929/building-a-facial-recognition-system-with-facenet-b9c249c2388a

knut0815 / facial-recognition Goto Github PK