Face Recognition-Based Attendance System with source code — Flask App — With GUI

Abhishek Sharma
6 min readSep 17, 2022

So guys here comes the most awaited project of machine learning Face Recognition-based Attendance System. As the name says this project takes attendance using biometrics (in this case face) and is one of the most famous projects among college students out there.

I have tried to make the project the easiest way possible. So without any further due, Let’s do it…

See the working of the project here — https://youtu.be/A_fqShFAS64

Snapshots of our App…

Face Recognition-based Attendance System Home Page…

List Users Page…

Attendance Sheet

Code files for our Face Recognition-based Attendance System


import cv2
import os
from flask import Flask,request,render_template
from datetime import date
from datetime import datetime
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import joblib

#### Defining Flask App
app = Flask(__name__)

#### Saving Date today in 2 different formats
def datetoday():
return date.today().strftime("%m_%d_%y")
def datetoday2():
return date.today().strftime("%d-%B-%Y")

#### Initializing VideoCapture object to access WebCam
face_detector = cv2.CascadeClassifier('static/haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)

#### If these directories don't exist, create them
if not os.path.isdir('Attendance'):
if not os.path.isdir('static/faces'):
if f'Attendance-{datetoday()}.csv' not in os.listdir('Attendance'):
with open(f'Attendance/Attendance-{datetoday()}.csv','w') as f:

#### get a number of total registered users
def totalreg():
return len(os.listdir('static/faces'))

#### extract the face from an image
def extract_faces(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_points = face_detector.detectMultiScale(gray, 1.3, 5)
return face_points

#### Identify face using ML model
def identify_face(facearray):
model = joblib.load('static/face_recognition_model.pkl')
return model.predict(facearray)

#### A function which trains the model on all the faces available in faces folder
def train_model():
faces = []
labels = []
userlist = os.listdir('static/faces')
for user in userlist:
for imgname in os.listdir(f'static/faces/{user}'):
img = cv2.imread(f'static/faces/{user}/{imgname}')
resized_face = cv2.resize(img, (50, 50))
faces = np.array(faces)
knn = KNeighborsClassifier(n_neighbors=5)

#### Extract info from today's attendance file in attendance folder
def extract_attendance():
df = pd.read_csv(f'Attendance/Attendance-{datetoday()}.csv')
names = df['Name']
rolls = df['Roll']
times = df['Time']
l = len(df)
return names,rolls,times,l

#### Add Attendance of a specific user
def add_attendance(name):
username = name.split('_')[0]
userid = name.split('_')[1]
current_time = datetime.now().strftime("%H:%M:%S")

df = pd.read_csv(f'Attendance/Attendance-{datetoday()}.csv')
if int(userid) not in list(df['Roll']):
with open(f'Attendance/Attendance-{datetoday()}.csv','a') as f:

################## ROUTING FUNCTIONS #########################
#### Our main page
def home():
names,rolls,times,l = extract_attendance()
return render_template('home.html',names=names,rolls=rolls,times=times,l=l,totalreg=totalreg(),datetoday2=datetoday2())

#### This function will run when we click on Take Attendance Button
def start():
if 'face_recognition_model.pkl' not in os.listdir('static'):
return render_template('home.html',totalreg=totalreg(),datetoday2=datetoday2(),mess='There is no trained model in the static folder. Please add a new face to continue.')
cap = cv2.VideoCapture(0)
ret = True
while ret:
ret,frame = cap.read()
if extract_faces(frame)!=():
(x,y,w,h) = extract_faces(frame)[0]
cv2.rectangle(frame,(x, y), (x+w, y+h), (255, 0, 20), 2)
face = cv2.resize(frame[y:y+h,x:x+w], (50, 50))
identified_person = identify_face(face.reshape(1,-1))[0]
cv2.putText(frame,f'{identified_person}',(30,30),cv2.FONT_HERSHEY_SIMPLEX,1,(255, 0, 20),2,cv2.LINE_AA)
if cv2.waitKey(1)==27:
names,rolls,times,l = extract_attendance()
return render_template('home.html',names=names,rolls=rolls,times=times,l=l,totalreg=totalreg(),datetoday2=datetoday2())

#### This function will run when we add a new user
def add():
newusername = request.form['newusername']
newuserid = request.form['newuserid']
userimagefolder = 'static/faces/'+newusername+'_'+str(newuserid)
if not os.path.isdir(userimagefolder):
cap = cv2.VideoCapture(0)
i,j = 0,0
while 1:
_,frame = cap.read()
faces = extract_faces(frame)
for (x,y,w,h) in faces:
cv2.rectangle(frame,(x, y), (x+w, y+h), (255, 0, 20), 2)
cv2.putText(frame,f'Images Captured: {i}/50',(30,30),cv2.FONT_HERSHEY_SIMPLEX,1,(255, 0, 20),2,cv2.LINE_AA)
if j%10==0:
name = newusername+'_'+str(i)+'.jpg'
if j==500:
cv2.imshow('Adding new User',frame)
if cv2.waitKey(1)==27:
print('Training Model')
names,rolls,times,l = extract_attendance()
return render_template('home.html',names=names,rolls=rolls,times=times,l=l,totalreg=totalreg(),datetoday2=datetoday2())

#### Our main function which runs the Flask App
if __name__ == '__main__':
  • Line 1–9: We are importing the required libraries.
  • Line 11–12: Defining the Flask App.
  • Line 15–19: Functions that return today’s date strings to use in the program ahead.
  • Line 22–24: Initializing VideoCapture object to access WebCam.
  • Line 27–34: Checking if the required folders are in place or not, If not create them.
  • Line 37–39: A function that calculates the number of total registered users.
  • Line 42–46: A function that extracts the face from an image.
  • Line 49–52: A function that Identifies face using ML model.
  • Line 55–69: A function that trains the model on all the faces available in the faces folder.
  • Line 72–79: A function that extracts info from today’s attendance file in the attendance folder.
  • Line 82–91: A function that adds the Attendance of a specific user in our today’s Attendance file.

Routing Functions:

  • Line 96–100: Our main page routing function.
  • Line 103–126: This function will run when we click on Take Attendance Button.
  • Line 129–160: This function will run when we add a new user.
  • Line 163–165: Our main function which runs the Flask App.


<!doctype html>
<html lang="en">
<style type='text/css'>
* {
padding: 0;
margin: 0;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;

body {
background-image: url('https://cutewallpaper.org/21/1920-x-1080-gif/1920x1080-Wallpapercartoon-Wallpapers-Driverlayer-Search-.gif');
background-size: cover;
font-family: sans-serif;
margin-top: 40px;
height: 100vh;
padding: 0;
margin: 0;
table {
border: 1px;
font-family: arial, sans-serif;
border-collapse: collapse;
width: 86%;
margin: auto;
th {
border: 1px solid black !important;
padding: 5px;
tr:nth-child(even) {
background-color: #dddddd;

<!-- Required meta tags -->
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
<!-- Bootstrap CSS -->
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/css/bootstrap.min.css" rel="stylesheet"
integrity="sha384-eOJMYsd53ii+scO/bJGFsiCZc+5NDVN2yr8+0RDqr0Ql0h+rP48ckxlpbzKgwra6" crossorigin="anonymous">
<title>Face Recognition Based Attendance System</title>
<div class='mt-3 text-center'>
<h1 style="width: auto;margin: auto;color: white;padding: 11px;font-size: 44px;">Face Recognition Based
Attendance System</h1>
{% if mess%}
<p class="text-center" style="color: red;font-size: 20px;">{{ mess }}</p>
{% endif %}
<div class="row text-center" style="padding: 20px;margin: 20px;">
<div class="col"
style="border-radius: 20px;padding: 0px;background-color:rgb(211,211,211,0.5);margin:0px 10px 10px 10px;min-height: 400px;">
<h2 style="border-radius: 20px 20px 0px 0px;background-color: #0b4c61;color: white;padding: 10px;">Today's
Attendance <i class="material-icons">assignment</i></h2>
<a style="text-decoration: none;max-width: 300px;" href="/start">
style="font-size: 24px;font-weight: bold;border-radius: 10px;width:490px;padding: 10px;margin-top: 30px;margin-bottom: 30px;"
type='submit' class='btn btn-primary'>Take Attendance <i
<table style="background-color: white;">
<td><b>S No</b></td>
{% if l %}
{% for i in range(l) %}
<td>{{ i+1 }}</td>
<td>{{ names[i] }}</td>
<td>{{ rolls[i] }}</td>
<td>{{ times[i] }}</td>
{% endfor %}
{% endif %}
<div class="col"
style="border-radius: 20px;padding: 0px;background-color:rgb(211,211,211,0.5);margin:0px 10px 10px 10px;height: 400px;">
<form action='/add' method="POST" enctype="multipart/form-data">
<h2 style="border-radius: 20px 20px 0px 0px;background-color: #0b4c61;color: white;padding: 10px;">Add
New User <i class="material-icons">control_point_duplicate</i></h2>
<label style="font-size: 20px;"><b>Enter New User Name*</b></label>
<input type="text" id="newusername" name='newusername'
style="font-size: 20px;margin-top:10px;margin-bottom:10px;" required>
<label style="font-size: 20px;"><b>Enter New User Id*</b></label>
<input type="number" id="newusereid" name='newuserid'
style="font-size: 20px;margin-top:10px;margin-bottom:10px;" required>
<button style="width: 232px;margin-top: 20px;font-size: 20px;" type='submit' class='btn btn-dark'>Add
New User
<h5 style="padding: 25px;"><i>Total Users in Database: {{totalreg}}</i></h5>


Source Code

Visit my blog for source code — https://machinelearningprojects.net/face-recognition-based-attendance-system/#Source_Code

How to Run the project

Check out this video for running this project — https://youtu.be/y4lkdSQgr0I

Do let me know if there’s any query regarding the Face Recognition-based Attendance System by contacting me via email or LinkedIn.

So this is all for this blog folks, thanks for reading it and I hope you are taking something with you after reading this and till the next time…


Check out my other machine learning projects, deep learning projects, computer vision projects, NLP projects, Flask projects at machinelearningprojects.net.