Face Recognize Modal from Webcam

Posted By :Arun Singh |28th April 2019

The Face detection technology offers a plethora of potential applications in real-world use cases.  In this blog we will focus on the webcam face detection application and gloss over upon how the algorithms in it actually work. If you want to know more about face detection then you go through https://www.analyticsvidhya.com/blog/2018/06/understanding-building-object-detection-model-python/

Before start face detection model ourselves, we will get into the technical details of that let's discuss some other use cases.

Small example of face detection is unlock our smart phones with the face unlock feature. The same thing we can be implemented on a large scale enable cameras to capture images and detect faces. We can figure out a way so that you don't need to carry any ID card or finger print scan to get access. Face Detection application will helps to figure out by faces. In this application person have to look to camera and it will automatically detect whether he/she should be allowed to enter or not.

Software Setup

Step 1: Install Python version 2.4 or 3.5 from here https://www.anaconda.com/distribution/

Step 2: Install OpenCV
             OpenCV is a library target at building computer vision application.

pip install opencv-python

Step 3: Install face_recognition API
             We used face_recognition library its simple face recognition API for Python. 

pip install dlib
pip install face_recognition

Now start code implementation

First create a file face_detection_from_webcam.py and then copy the code given below.

import face_recognition
import cv2
import numpy as np

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

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

# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("arun.png")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("Satender.png")
biden_face_encoding = face_recognition.face_encodings(biden_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 = video_capture.read()

    # 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)
            print matches
            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

Then run this python file

python face_detection_from_webcam.py

To summarize, this is what our above code did:

  • First, we defined the hardware on which have the webcam.
  • From this, we webcam will be open.
  • Then we processed each faces which captured by webcam.
  • Finally, we detect exist user name, along with the face locations.

If you want detect face from video so you follow this blog for more information: https://www.analyticsvidhya.com/blog/2018/12/introduction-face-detection-video-deep-learning-python/ 

Thanks for reading!

About Author

Arun Singh

Arun is a MEAN stack developer. He has a fastest and efficient way of problem solving techniques. He is very good in JavaScript and also have a little bit knowledge of Java and Python.

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