/Real-time-Smart-Face-Logger

Real-time Smart Face Logger is an AI-based system that detects and logs faces in real-time using computer vision, enabling automated attendance and monitoring solutions.

Primary LanguagePython

Real-time Smart Face Logger

A Python-based real-time face recognition system that captures video input via webcam, detects faces, and logs the recognized individuals' names and timestamps into a CSV file. This project uses the face_recognition and OpenCV libraries to perform real-time detection and logging.

Features

  • Real-time face detection: Captures frames from the webcam, detects faces, and recognizes them in real-time.
  • Face recognition: Compares faces in the video feed with pre-loaded known faces and labels them.
  • Attendance logging: Records the recognized person's name and time of recognition into a CSV file.
  • CSV file auto-naming: The CSV filename is dynamically generated based on the date.
  • Face bounding box: Draws a rectangular box around the detected face and displays the name of the recognized person on the video feed.

Prerequisites

Before running the project, ensure that you have the following installed on your system:

  • Python 3.x
  • OpenCV (opencv-python)
  • NumPy (numpy)
  • Face Recognition (face-recognition - depends on dlib)

Installation

  1. Clone this repository or download the project files.

  2. Install the required dependencies:

    pip install opencv-python numpy face-recognition

  3. Download or prepare the images of the individuals you want the system to recognize. Place them in the project directory and update the file paths in the script.

Usage

  1. Prepare Images for Recognition Ensure the images of the known people are stored locally. Load these images in the script and create encodings for them as follows:
# Example:
deepanshi_image = face_recognition.load_image_file("deep.jpg")
deepanshi_encoding = face_recognition.face_encodings(deepanshi_image)[0]

known_face_encodings = [deepanshi_encoding]
known_face_names = ["Deepanshi"]
#Similarly
known_face_encodings = [deepanshi_encoding, sundarP_encoding]
known_face_names = ["Deepanshi", "Sundar Pichai"]

  1. Run the script:

    python face.py

  2. The system will start the webcam feed, detect faces, and log recognized faces into a CSV file named after the date.

    04-09-2024.csv

    Name Time
    Deepanshi 15:32:10
    Sundar Pichai 15:32:10

  3. Press q to exit the webcam feed and stop the program.

Customization

  • Adding more known faces: Add images of new people, encode them using face_recognition.face_encodings, and append them to known_face_encodings and known_face_names.

  • CSV Logging: The CSV file is named using the format {date}.csv. You can modify the logging format or fields in the code.

Dependencies

  • OpenCV - For handling video capture and frame processing.
  • NumPy - For handling numerical operations.
  • Face Recognition - A powerful library built on top of dlib for face detection and recognition.

Known Issues

  • Face Detection Accuracy: If the face detection accuracy is low, try using higher-resolution images or adjusting the scale_factor for resizing the frame.
  • Lighting Conditions: Poor lighting may affect the performance of face detection. Ensure proper lighting in the room where the system is used.