Face Recognition Door Lock System Using OpenCV on Raspberry Pi


Face recognition door lock system is capable of making decisions based on facial recognition technology. The system uses a webcam and a Raspberry Pi. It is capable of performing all the facial recognition stages on its own such as face detection, features extraction, face recognition using OpenCV libraries.


This video shows how we make a face recognition door lock system using OpenCV on Raspberry Pi.

Hardware Preparation

This is the list of items used in the video.

Sample Program

This is python3 sample program for Face Recognition Door Lock System using Raspberry Pi. You can use it with Thonny Python IDE.

#! /usr/bin/python
# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import face_recognition
import imutils
import pickle
import time
import cv2
import RPi.GPIO as GPIO
RELAY = 17
#Initialize 'currentname' to trigger only when a new person is identified.
currentname = "unknown"
#Determine faces from encodings.pickle file model created from train_model.py
encodingsP = "encodings.pickle"
#use this xml file
cascade = "haarcascade_frontalface_default.xml"
# load the known faces and embeddings along with OpenCV's Haar
# cascade for face detection
print("[INFO] loading encodings + face detector…")
data = pickle.loads(open(encodingsP, "rb").read())
detector = cv2.CascadeClassifier(cascade)
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream…")
vs = VideoStream(src=0).start()
#vs = VideoStream(usePiCamera=True).start()
# start the FPS counter
fps = FPS().start()
prevTime = 0
doorUnlock = False
# loop over frames from the video file stream
while True:
# grab the frame from the threaded video stream and resize it
# to 500px (to speedup processing)
frame = vs.read()
frame = imutils.resize(frame, width=500)
# convert the input frame from (1) BGR to grayscale (for face
# detection) and (2) from BGR to RGB (for face recognition)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# detect faces in the grayscale frame
rects = detector.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=5, minSize=(30, 30),
# OpenCV returns bounding box coordinates in (x, y, w, h) order
# but we need them in (top, right, bottom, left) order, so we
# need to do a bit of reordering
boxes = [(y, x + w, y + h, x) for (x, y, w, h) in rects]
# compute the facial embeddings for each face bounding box
encodings = face_recognition.face_encodings(rgb, boxes)
names = []
# loop over the facial embeddings
for encoding in encodings:
# attempt to match each face in the input image to our known
# encodings
matches = face_recognition.compare_faces(data["encodings"],
name = "Unknown" #if face is not recognized, then print Unknown
# check to see if we have found a match
if True in matches:
# find the indexes of all matched faces then initialize a
# dictionary to count the total number of times each face
# was matched
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
# to unlock the door
prevTime = time.time()
doorUnlock = True
print("door unlock")
# loop over the matched indexes and maintain a count for
# each recognized face face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1
# determine the recognized face with the largest number
# of votes (note: in the event of an unlikely tie Python
# will select first entry in the dictionary)
name = max(counts, key=counts.get)
#If someone in your dataset is identified, print their name on the screen
if currentname != name:
currentname = name
# update the list of names
#lock the door after 5 seconds
if doorUnlock == True and time.time() prevTime > 5:
doorUnlock = False
print("door lock")
# loop over the recognized faces
for ((top, right, bottom, left), name) in zip(boxes, names):
# draw the predicted face name on the image – color is in BGR
cv2.rectangle(frame, (left, top), (right, bottom),
(0, 255, 0), 2)
y = top 15 if top 15 > 15 else top + 15
cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
.8, (255, 0, 0), 2)
# display the image to our screen
cv2.imshow("Facial Recognition is Running", frame)
key = cv2.waitKey(1) & 0xFF
# quit when 'q' key is pressed
if key == ord("q"):
# update the FPS counter
# stop the timer and display FPS information
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup

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Thanks for reading this tutorial. If you have any technical inquiries, please post at Cytron Technical Forum.

Please be reminded, this tutorial is prepared for you to try and learn.
You are encouraged to improve the code for better application.

3 thoughts on “Face Recognition Door Lock System Using OpenCV on Raspberry Pi”

  1. Traceback (most recent call last):
    File “/home/pi/face_Recognization.py”, line 31, in
    data = pickle.loads(open(encodingsP, “rb”).read())
    FileNotFoundError: [Errno 2] No such file or directory: ‘encodings.pickle’

    please help me in this error


    can i upgrade this project by adding alexa so that we can control using voice command?

  3. Hi,

    While trying the above code, My solenoid lock is open throughout the execution,.
    Please let me know how to have it closed in the mentioned time frame and also as per face recognition.


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