FaceSync-Attendance-Automation

Image Collection Script: imagecollect.py

Overview

The imagecollect.py script is responsible for collecting facial images to train a facial recognition model. It captures images from the webcam, detects faces, and stores the face data in a pickled file for future use.

Dependencies

• OpenCV: Used for webcam access and image processing.
• NumPy: Handles and processes arrays of facial images.
• Pickle: Serializes and stores face data.
• User Input: Collects user input for labeling the collected data.

Script Execution

1. Webcam Initialization:

   • Opens a connection to the default webcam (index 0).

2. Face Detection:

   • Utilizes the Haar Cascade Classifier to detect faces in the webcam feed.
   • Draws rectangles around detected faces for visual indication.

3. Data Collection:

   • Captures facial data by cropping and resizing the detected faces.
   • Limits the collection to 100 images, sampled every 10 frames.

4. User Input:

   • Requests the user to input their name for labeling the collected data.

5. Data Storage:

   • Converts the collected facial data into a NumPy array.
   • Reshapes the array and stores it in a Pickle file (faces_dataset.pkl).
   • Manages a separate Pickle file for storing corresponding names (names.pkl).

6. Usage Instructions:

   • Run the script in a Python environment.
   • Follow on-screen instructions to input your name and allow the webcam to capture facial images.
   • Press the 'k' key to stop the data collection process or wait until 100 images are collected.
   • The collected facial data and corresponding names are stored in the data/ directory.

Face Recognition Script: detection.py

Overview

The detection.py script is designed for real-time face recognition and attendance marking. It utilizes a pre-trained K-Nearest Neighbors classifier with the previously collected data. The script captures video feed from the webcam, detects faces, predicts identities, and marks attendance with a timestamp.

Dependencies

• OpenCV: For webcam access and image processing.
• Scikit-learn: Utilizes the K-Nearest Neighbors classifier for face recognition.
• Pickle: Reads pre-collected facial data and labels from Pickle files.
• CSV: Manages attendance records through CSV file operations.
• Datetime: Obtains the current date and time for timestamping attendance.
• Text-to-Speech (TTS): Provides spoken notifications.

Script Execution

1. Webcam Initialization:

   • Opens a connection to the default webcam (index 0).

2. Classifier and Data Loading:

   • Loads pre-trained face data and labels from Pickle files (faces_dataset.pkl and names.pkl).
   • Initializes a K-Nearest Neighbors classifier.

3. Attendance Marking:

   • Captures video frames from the webcam.
   • Detects faces using the Haar Cascade Classifier.
   • Predicts identities using the K-Nearest Neighbors classifier.
   • Marks attendance with the recognized identity, date, and timestamp.

4. CSV File Handling:

   • Checks for the presence of an attendance CSV file for the current date.
   • Appends or creates a new CSV file accordingly.
   • Records attendance information in the CSV file.

5. User Interaction:

   • Pressing 't' marks attendance and provides a spoken notification.
   • Pressing 'k' exits the script with a farewell spoken notification.

Files Generated

• data/faces_dataset.pkl: Pickle file containing the collected facial data.
• data/names.pkl: Pickle file containing corresponding names.
• attendence/attendence_DD-MM-YYYY.csv: CSV file containing attendance records for the respective date.

Notes on how to execute

• Both scripts are part of a facial recognition attendance system project.
• First run image_collect.py it prepares the data for the recognition script and make sure to check if files named faces_dataset.pk and names.pkl are generated in the data folder or not
• Then run detection.py to mark attendance in real-time by pressing t on the keyboard and if you wish to quit before press k.
• Then check for the CSV file generated inside the attendance folder

Feel free to use code Thank-you