This project implements a face recognition-based attendance system using Python, OpenCV, and the face_recognition library. It uses a webcam feed to recognize faces from a set of pre-trained images, and records the attendance of the recognized individuals in a CSV file with timestamps.
Ensure you have Python 3.x installed on your system. You will also need to install the following dependencies:
opencv-pythonnumpyface_recognition
You can install these dependencies using pip:
pip install opencv-python numpy face_recognitionClone this repository using the following command:
git clone <repository-url>
cd <repository-directory>Replace `` with the actual URL of your GitHub repository.
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Training Images:
- Create a folder called
Training_imagesin your project directory. - Place the images of individuals you want to recognize in this folder. Make sure each image file is named after the person it represents (e.g.,
John.jpg,Jane.jpg).
- Create a folder called
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Run the Script: Run the Python script to start the face recognition-based attendance system:
python main.py
- The recognized individuals' names, along with the time of recognition, are recorded in a CSV file called
Attendance.csv. - If a person is already marked as present, they will not be marked again during the same session.
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Loading Training Images: The script reads the images from the
Training_imagesdirectory and converts them into face encodings using theface_recognitionlibrary.path = 'Training_images' images = [] classNames = [] myList = os.listdir(path) for cl in myList: curImg = cv2.imread(f'{path}/{cl}') images.append(curImg) classNames.append(os.path.splitext(cl)[0])
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Finding Encodings: The
findEncodingsfunction converts each image into a list of encodings. These encodings are later used to compare with live video frames to recognize faces.def findEncodings(images): encodeList = [] for img in images: img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) encode = face_recognition.face_encodings(img)[0] encodeList.append(encode) return encodeList
- Marking Attendance in CSV: When a face is recognized, the name is recorded in
Attendance.csvalong with the time of recognition. The system ensures that a person is not marked more than once in a session.
def markAttendance(name):
with open('Attendance.csv', 'r+') as f:
myDataList = f.readlines()
nameList = []
for line in myDataList:
entry = line.split(',')
nameList.append(entry[0])
if name not in nameList:
now = datetime.now()
dtString = now.strftime('%H:%M:%S')
f.writelines(f'\n{name},{dtString}')-
Capturing Video: The script uses
cv2.VideoCapture(0)to capture live video from the webcam. -
Face Detection and Recognition:
- The script processes the video feed in real-time, detects faces, and compares them with the pre-encoded face data.
- If a match is found, the person's name is displayed on the video frame and recorded in the attendance CSV.
while True:
success, img = cap.read()
imgS = cv2.resize(img, (0, 0), None, 0.25, 0.25)
imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
facesCurFrame = face_recognition.face_locations(imgS)
encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
matchIndex = np.argmin(faceDis)
if matches[matchIndex]:
name = classNames[matchIndex].upper()
y1, x2, y2, x1 = faceLoc
y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.rectangle(img, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
cv2.putText(img, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
markAttendance(name)
cv2.imshow('Webcam', img)
cv2.waitKey(1)-
Error: `list index out of range`
- This error occurs if no face is detected in the training images. Ensure that all the images in the
Training_imagesfolder contain clear, recognizable faces. - You can add a check before accessing
face_recognition.face_encodings(img)[0]to ensure that faces are detected in the image.
- This error occurs if no face is detected in the training images. Ensure that all the images in the
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Webcam Not Opening
- Ensure that your webcam is properly connected and that
cv2.VideoCapture(0)is using the correct webcam index. - If you have multiple cameras, you may need to adjust the index (e.g.,
cv2.VideoCapture(1)).
- Ensure that your webcam is properly connected and that
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No Face Detected in Video
- Ensure the video is clear and well-lit. Poor lighting can affect the ability of the model to detect faces.
- Test with a variety of angles and distances to make sure the face is within the frame.
This project is licensed under the MIT License - see the LICENSE file for details.
This project uses the following open-source libraries:
- OpenCV
- face_recognition
- NumPy