Deep Learning Face Recognition from Live Webcam with a code example

This is a example of running face recognition on live video from your webcam.

You can download project at github

We will use library

Face Recognition is built using dlib's state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38%

We will use a simple face_recognition command line tool that lets us do face recognition on a folder of images from the command line!


Train model:

Prepare a set of images of the known people you want to recognize. Organize the images in a single directory with a sub-directory for each known person.

/images/train/{Person Name}/image.jpg

  • train
    • Ivan
      • image1.jpg
    • John
      • image1.jpg

Image naming does not matter...

Folder name will be used as label

Train image example





import face_recognition
import cv2
import numpy as np

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.
# ivan_image = face_recognition.load_image_file("ivan.jpg")
ivan_image = face_recognition.load_image_file("images/train/Ivan/1.jpg")
ivan_face_encoding = face_recognition.face_encodings(ivan_image)[0]

# Load a second sample picture and learn how to recognize it.
# maja_image = face_recognition.load_image_file("maja.jpg")
maja_image = face_recognition.load_image_file("images/train/Maja/3.jpg")
maja_face_encoding = face_recognition.face_encodings(maja_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
known_face_names = [

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    # Grab a single frame of video
    ret, frame =

    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"

            # # If a match was found in known_face_encodings, just use the first one.
            # if True in matches:
            #     first_match_index = matches.index(True)
            #     name = known_face_names[first_match_index]

            # Or instead, use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]


    process_this_frame = not process_this_frame

    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):

# Release handle to the webcam