FACE ATTENDENCE SYSTEM MAKING LOGIC

FACE RECOGNITION SYSTEM THAT MACHING FACE FROM STORED MULTIPLE PERSON IMAGE WITH MAXIMUM ACCURACY 

Python Code Is Now Here  : (Write and Test It Proper Indentation.)

import cv2

import face_recognition

import os

import numpy as np

# Load the images from the folder

folder_path = '../userPhoto'

image_files = os.listdir(folder_path)

# Initialize arrays to store known face encodings and names

known_encodings = []

known_names = []

known_images = []

# Load the known face images and compute their encodings

for image_file in image_files:

    image_path = os.path.join(folder_path, image_file)

    image = face_recognition.load_image_file(image_path)

    face_locations = face_recognition.face_locations(image)

    if len(face_locations) > 0:

        encoding = face_recognition.face_encodings(image, face_locations)[0]

        known_encodings.append(encoding)

        known_names.append(os.path.splitext(image_file)[0])

        known_images.append(cv2.resize(image, (100, 100)))  # Resize image for thumbnail display

# Initialize the webcam

video_capture = cv2.VideoCapture(0)

while True:

    # Capture frame-by-frame from the webcam

    ret, frame = video_capture.read()

    # Convert the frame to RGB for face recognition

    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Detect faces in the frame

    face_locations = face_recognition.face_locations(rgb_frame)

    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Iterate over detected faces

    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):

        # Compare the face with known encodings

        distances = face_recognition.face_distance(known_encodings, face_encoding)

        min_distance_index = np.argmin(distances)

        min_distance = distances[min_distance_index]

        if min_distance <= 0.45:  # Adjust the threshold as needed

            name = known_names[min_distance_index]

            accuracy = (1 - min_distance) * 100  # Calculate accuracy percentage

            thumbnail = known_images[min_distance_index]

            # Draw a rectangle around the face

            cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)

            # Display the name and accuracy below the face rectangle

            text = f"{name}: {accuracy:.2f}%"

            cv2.putText(frame, text, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 1)

            # Display the thumbnail image in the top right corner

            frame[10:110, frame.shape[1] - 110:frame.shape[1] - 10] = thumbnail

        else:

            name = "Unknown"

            # Draw a rectangle around the face

            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

            # Display the name below the face rectangle

            cv2.putText(frame, name, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)

    # Display the resulting frame

    cv2.imshow('Face Recognition', frame)

    # Quit the program if 'q' is pressed

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

# Release the webcam and close windows

video_capture.release()

cv2.destroyAllWindows()