ML Face Recognition project

 Face recognition is the process of identifying or verifying the identity of an individual using their facial features. With the advancement of technology, face recognition systems have become popular in various fields, including security, surveillance, and biometrics. In this project, we propose a face recognition system using a Graphical User Interface (GUI) that allows for user-friendly interaction.

The proposed system employs a combination of facial feature extraction and machine learning algorithms to recognize faces in real-time. The system begins by capturing a live video feed from a camera and extracting facial features from the video frames. These features are then compared to a database of known faces using a machine learning algorithm, which is trained to recognize facial features and patterns.

The GUI provides an intuitive user interface for the system, allowing users to interact with the system and view the results of the face recognition process. The system displays the recognized face along with the name or ID of the person in real-time.

The proposed system has several potential applications, including security systems for public places, attendance systems for schools or businesses, and access control systems for restricted areas. Overall, the face recognition system using GUI provides a fast, reliable, and user-friendly solution for face recognition.

CODE: 👇👇 # importing libraries import tkinter as tk from tkinter import Message, Text import cv2 import os import shutil import csv import numpy as np from PIL import Image, ImageTk import pandas as pd import datetime import time import tkinter.ttk as ttk import tkinter.font as font from pathlib import Path from cv2 import face # import cv2.face window = tk.Tk() window.title("Face_Recogniser") window.configure(background='white') window.grid_rowconfigure(0, weight=1) window.grid_columnconfigure(0, weight=1) message = tk.Label( window, text="Face-Recognition-System", bg="green", fg="white", width=50, height=3, font=('times', 30, 'bold')) message.place(x=200, y=20) lbl = tk.Label(window, text="No.", width=20, height=2, fg="green", bg="white", font=('times', 15, ' bold ')) lbl.place(x=400, y=200) txt = tk.Entry(window, width=20, bg="white", fg="green", font=('times', 15, ' bold ')) txt.place(x=700, y=215) lbl2 = tk.Label(window, text="Name", width=20, fg="green", bg="white", height=2, font=('times', 15, ' bold ')) lbl2.place(x=400, y=300) txt2 = tk.Entry(window, width=20, bg="white", fg="green", font=('times', 15, ' bold ')) txt2.place(x=700, y=315) # The function below is used for checking # whether the text below is number or not ? def is_number(s): try: float(s) return True except ValueError: pass try: import unicodedata unicodedata.numeric(s) return True except (TypeError, ValueError): pass return False # Take Images is a function used for creating # the sample of the images which is used for # training the model. It takes 60 Images of # every new user. def TakeImages(): # Both ID and Name is used for recognising the Image Id = (txt.get()) name = (txt2.get()) # Checking if the ID is numeric and name is Alphabetical if(is_number(Id) and name.isalpha()): # Opening the primary camera if you want to access # the secondary camera you can mention the number # as 1 inside the parenthesis cam = cv2.VideoCapture(0) # Specifying the path to haarcascade file harcascadePath = "data\haarcascade_frontalface_default.xml" # Creating the classier based on the haarcascade file. detector = cv2.CascadeClassifier(harcascadePath) # Initializing the sample number(No. of images) as 0 sampleNum = 0 while(True): # Reading the video captures by camera frame by frame ret, img = cam.read() # Converting the image into grayscale as most of # the processing is done in gray scale format gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # It converts the images in different sizes # (decreases by 1.3 times) and 5 specifies the # number of times scaling happens faces = detector.detectMultiScale(gray, 1.3, 5) # For creating a rectangle around the image for (x, y, w, h) in faces: # Specifying the coordinates of the image as well # as color and thickness of the rectangle. # incrementing sample number for each image cv2.rectangle(img, (x, y), ( x + w, y + h), (255, 0, 0), 2) sampleNum = sampleNum + 1 # saving the captured face in the dataset folder # TrainingImage as the image needs to be trained # are saved in this folder cv2.imwrite( "TrainingImage\ "+name + "."+Id + '.' + str( sampleNum) + ".jpg", gray[y:y + h, x:x + w]) # display the frame that has been captured # and drawn rectangle around it. cv2.imshow('frame', img) # wait for 100 milliseconds if cv2.waitKey(100) & 0xFF == ord('q'): break # break if the sample number is more than 60 elif sampleNum > 60: break # releasing the resources cam.release() # closing all the windows cv2.destroyAllWindows() # Displaying message for the user res = "Images Saved for ID : " + Id + " Name : " + name # Creating the entry for the user in a csv file row = [Id, name] with open(r'UserDetails\UserDetails.csv','a+') as csvFile: writer = csv.writer(csvFile) # Entry of the row in csv file writer.writerow(row) csvFile.close() message.configure(text=res) else: if(is_number(Id)): res = "Enter Alphabetical Name" message.configure(text=res) if(name.isalpha()): res = "Enter Numeric Id" message.configure(text=res) # Training the images saved in training image folder def TrainImages(): # Local Binary Pattern Histogram is an Face Recognizer # algorithm inside OpenCV module used for training the image dataset recognizer = cv2.face.LBPHFaceRecognizer_create() # Specifying the path for HaarCascade file harcascadePath = "data\haarcascade_frontalface_default.xml" # creating detector for faces detector = cv2.CascadeClassifier(harcascadePath) # Saving the detected faces in variables faces, Id = getImagesAndLabels("TrainingImage") # Saving the trained faces and their respective ID's # in a model named as "trainer.yml". recognizer.train(faces, np.array(Id)) recognizer.save("TrainingImageLabel\Trainer.yml") # Displaying the message res = "Image Trained" message.configure(text=res) def getImagesAndLabels(path): # get the path of all the files in the folder imagePaths = [os.path.join(path, f) for f in os.listdir(path)] faces = [] # creating empty ID list Ids = [] # now looping through all the image paths and loading the # Ids and the images saved in the folder for imagePath in imagePaths: # loading the image and converting it to gray scale pilImage = Image.open(imagePath).convert('L') # Now we are converting the PIL image into numpy array imageNp = np.array(pilImage, 'uint8') # getting the Id from the image Id = int(os.path.split(imagePath)[-1].split(".")[1]) # extract the face from the training image sample faces.append(imageNp) Ids.append(Id) return faces, Ids # For testing phase def TrackImages(): recognizer = cv2.face.LBPHFaceRecognizer_create() # Reading the trained model recognizer.read("TrainingImageLabel\Trainer.yml") harcascadePath = "data\haarcascade_frontalface_default.xml" faceCascade = cv2.CascadeClassifier(harcascadePath) # getting the name from "userdetails.csv" df = pd.read_csv(r"UserDetails\UserDetails.csv") cam = cv2.VideoCapture(0) font = cv2.FONT_HERSHEY_SIMPLEX while True: ret, im = cam.read() gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) faces = faceCascade.detectMultiScale(gray, 1.2, 5) for(x, y, w, h) in faces: cv2.rectangle(im, (x, y), (x + w, y + h), (225, 0, 0), 2) Id, conf = recognizer.predict(gray[y:y + h, x:x + w]) if(conf < 50): # print(df.loc[df['A'] == Id]['Name'].values) print(df) aa = df.loc[df['Id'] == Id]['Name'].values tt = str(Id)+"-"+aa else: Id = 'Unknown' tt = str(Id) if(conf > 75): noOfFile = len(os.listdir("ImagesUnknown"))+1 cv2.imwrite("ImagesUnknown\Image" + str(noOfFile) + ".jpg", im[y:y + h, x:x + w]) cv2.putText(im, str(tt), (x, y + h), font, 1, (255, 255, 255), 2) cv2.imshow('im', im) if (cv2.waitKey(1) == ord('q')): break cam.release() cv2.destroyAllWindows() takeImg = tk.Button(window, text="Sample", command=TakeImages, fg="white", bg="green", width=20, height=3, activebackground="Red", font=('times', 15, ' bold ')) takeImg.place(x=200, y=500) trainImg = tk.Button(window, text="Training", command=TrainImages, fg="white", bg="green", width=20, height=3, activebackground="Red", font=('times', 15, ' bold ')) trainImg.place(x=500, y=500) trackImg = tk.Button(window, text="Testing", command=TrackImages, fg="white", bg="green", width=20, height=3, activebackground="Red", font=('times', 15, ' bold ')) trackImg.place(x=800, y=500) quitWindow = tk.Button(window, text="Quit", command=window.destroy, fg="white", bg="green", width=20, height=3, activebackground="Red", font=('times', 15, ' bold ')) quitWindow.place(x=1100, y=500) window.mainloop()

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