Face detection is a computer vision technology that helps to locate/ visualize the human faces in images and predicts the identity of that face.
The Face Recognition System uses accurate computer algorithms to pick out the specific, distinctive details about a person’s face.
Such as the distance between the eyes or shape of the chin, are converted into a mathematical representations & compared to data of other faces which are collected in a face recognition database. The secret behind face recognition is a technique called ‘deep metric learning’.We had implemented it in our many live applications like for log in using face recognition , door unlock on the basis of face recognition and many more.
Create the encodings of the faces
It will generate encodings.pickle ( model file )
- For that Run -
python encode_faces.py
It is the process of training over the faces (images) present in the dataset
You can use any of the method either Hog or Cnn -
Hog ( Histogram oriented graphs ) - Faster Results CNN (Convolutional Neural Network ) - Slower but accurate results than that of the hog method
Note - Train everytime when new faces added to the dataset
Run the code to recognize the faces from the image as well as video
- To Recognize faces from input image
python recognize_faces_image.py --encodings encodings.pickle --image dataset/Ayaz/ayaz.jpg
- To Recognize faces from live video
python recognize_faces_video.py --encodings encodings.pickle
- To Reocgnize faces from input video file
python recognize_faces_video.py --encodings encodings.pickle --output output/ayaz.avi --display 0
Link to demo video.