Online-Exam-Proctoring-System. This project endeavors to create an automated proctoring system that monitors users via webcam and microphone during online examinations. It comprises two main components: vision-based and audio-based functionalities. Below, we detail the functionalities of the vision-based component without relying on external references: Prerequisites: Before running the programs in this repository, set up a virtual environment following these steps:

1. Create a virtual environment: python -m venv venv
2. Activate the virtual environment: cd ./venv/Scripts/activate (Windows users) source ./venv/bin/activate (Mac and Linux users 3.Install the requirements: pip install --upgrade pip (to upgrade pip) pip install -r requirements.txt Features:
3. Eye tracking: This feature detects left, right, or upward eye movements, enabling monitoring of users' gaze directions.
4. Mouth opening detection: By recording initial lip distances, the system identifies instances of mouth opening during examinations, ensuring compliance with exam rules.
5. Instance segmentation: This function counts individuals and distinguishes if no one or more than one person is detected in the exam environment, maintaining exam integrity.
6. Head pose estimation: By analyzing head orientation, this feature determines the user's gaze direction, aiding in monitoring their focus during the exam.
7. Face spoofing detection: Using advanced algorithms, the system identifies and flags any attempts at face spoofing or presenting false images during the exam. Face Detection: The project compares different face detection models, with OpenCV's DNN module providing the best results. The implementation is in face_detector.py, facilitating various functionalities within the system. Facial Landmarks: A Tensorflow-based model is utilized for facial landmark detection, replacing Dlib's model for improved accuracy. The implementation can be found in face_landmarks.py, enabling precise tracking of facial features.