This system automatically:

✔ Detects faces
✔ Recognizes known students/employees
✔ Marks attendance into CSV
✔ Stores date/time for each entry
✔ Prevents duplicate attendance for the same day

Perfect for office, classroom, labs, events, coaching centers, etc.

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🧠 Tech Stack

Component	Library
Face Detection	OpenCV
Face Recognition	face_recognition
Data Storage	CSV
Camera Capture	OpenCV
Encoding Faces	dlib (used internally by face_recognition)

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⚙️ Install Required Libraries

Run:

pip install opencv-python
pip install face_recognition
pip install numpy
pip install pandas

👉 face_recognition requires CMake + dlib
If error comes, I will give you auto installer for dlib.

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📁 Folder Structure

FaceAttendance/
│
├── images/
│     ├── Sameer.jpg
│     ├── John.jpg
│     └── Ayesha.jpg
│
├── attendance.csv
│
└── face_attendance.py

You must store clear front-facing photos in /images.

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🧩 FULL WORKING CODE (face_attendance.py)

import cv2
import numpy as np
import face_recognition
import os
import pandas as pd
from datetime import datetime

path = 'images'
images = []
classNames = []
myList = os.listdir(path)

print("Loading images...")

for cls in myList:
    curImg = cv2.imread(f'{path}/{cls}')
    images.append(curImg)
    classNames.append(os.path.splitext(cls)[0])

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

def markAttendance(name):
    df = pd.read_csv('attendance.csv')

    today = datetime.now().strftime('%Y-%m-%d')
    time_now = datetime.now().strftime('%H:%M:%S')

    if not ((df['Name'] == name) & (df['Date'] == today)).any():
        new_entry = pd.DataFrame([[name, today, time_now]], 
                                 columns=['Name', 'Date', 'Time'])
        df = pd.concat([df, new_entry], ignore_index=True)
        df.to_csv('attendance.csv', index=False)
        print(f"Attendance marked for {name}")
    else:
        print(f"{name} already marked today")

encodeListKnown = findEncodings(images)
print("Encoding Complete")

cap = cv2.VideoCapture(0)

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), (x2, y2+35), (0,255,0), cv2.FILLED)
            cv2.putText(img, name, (x1+6, y2+28), cv2.FONT_HERSHEY_SIMPLEX, 
                        1, (255,255,255), 2)

            markAttendance(name)

    cv2.imshow('Face Attendance', img)
    if cv2.waitKey(1) == 27:
        break

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📄 Create attendance.csv (Empty File)

Create attendance.csv with this header:

Name,Date,Time

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🎥 How It Works

1. Loads all face images from /images/
2. Converts them into encodings
3. Starts webcam
4. Detects a face
5. Compares with known encodings
6. Identifies person
7. Marks attendance in attendance.csv
8. Prevents duplicate attendance for the same day