Face Detection and Recognition System Source Code

pip install opencv-python
pip install face_recognition
pip install numpy

face-recognition-system/
├── encode_faces.py
├── recognize_faces.py
├── dataset/
│   ├── person1/
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   └── person2/
│       ├── image1.jpg
│       ├── image2.jpg
└── encodings.pickle

import face_recognition
import pickle
import cv2
import os

# Initialize the list of known encodings and known names
known_encodings = []
known_names = []

# Loop over the dataset
dataset_path = "dataset"
for person_name in os.listdir(dataset_path):
    person_folder = os.path.join(dataset_path, person_name)

    for image_name in os.listdir(person_folder):
        image_path = os.path.join(person_folder, image_name)
        print(f"Processing {image_path}")

        # Load the image and convert it from BGR (OpenCV ordering) to RGB (face_recognition ordering)
        image = cv2.imread(image_path)
        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # Detect the coordinates of the bounding boxes corresponding to each face in the input image
        boxes = face_recognition.face_locations(rgb_image, model="hog")

        # Compute the facial embedding for the face
        encodings = face_recognition.face_encodings(rgb_image, boxes)

        for encoding in encodings:
            known_encodings.append(encoding)
            known_names.append(person_name)

# Save the facial encodings + names to disk
data = {"encodings": known_encodings, "names": known_names}
with open("encodings.pickle", "wb") as f:
    f.write(pickle.dumps(data))

import face_recognition
import pickle
import cv2

# Load the known faces and embeddings
with open("encodings.pickle", "rb") as f:
    data = pickle.loads(f.read())

# Initialize the video stream and pointer to output video file
video_capture = cv2.VideoCapture(0)

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()
    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Find all the faces and face encodings in the current frame of video
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Loop over each face found in the frame to see if it's someone we know
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        matches = face_recognition.compare_faces(data["encodings"], face_encoding)
        name = "Unknown"

        face_distances = face_recognition.face_distance(data["encodings"], face_encoding)
        best_match_index = face_distances.argmin()
        if matches[best_match_index]:
            name = data["names"][best_match_index]

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 255, 0), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (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'):
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()