AI BASED EXAM PROCTORING SYSTEM

Overview: The AI-Based Exam Proctoring System is an advanced solution designed to ensure the integrity and security of online examinations. Leveraging cutting-edge artificial intelligence technologies, this system monitors students during exams to detect and prevent cheating, ensuring a fair and credible assessment process.

Features:

• Real-Time Monitoring: The system uses webcam and microphone inputs to continuously monitor the test-taker, ensuring they remain focused on the exam.
• Facial Recognition: Verifies the identity of the test-taker to prevent impersonation.
• Behavior Analysis: Detects suspicious behaviors such as looking away from the screen frequently, unusual movements, or multiple people in the room.
• Audio Analysis: Monitors for sounds that might indicate cheating, such as conversations or the use of unauthorized devices.
• Environment Scanning: Uses computer vision to scan the test-taker's surroundings for unauthorized materials or devices.
• Automated Alerts: Generates real-time alerts for any detected anomalies, allowing human proctors to review flagged events promptly.
• Post-Exam Review: Provides detailed reports and recordings of the exam session for further review and analysis.

Target Audience:

• Educational Institutions: Schools, colleges, and universities seeking a reliable method to administer online exams with integrity.
• Certification Bodies: Organizations that offer professional certifications and need to maintain high standards of exam security.
• Corporate Training Programs: Companies providing internal training and certification programs for employees, ensuring the credibility of the assessments.
• Online Course Providers: Platforms offering MOOCs (Massive Open Online Courses) and other e-learning programs that require secure testing environments.

By ensuring a secure and fair examination process, the AI-Based Exam Proctoring System helps educational and certification institutions uphold their standards and maintain the trust of their stakeholders.

Prerequisites:

To run the programs in this repository, follow these steps:

1. Create and activate a virtual environment:

   python -m venv venv

   • Windows users:

     cd ./venv/Scripts/activate

   • Mac and Linux users:

     source ./venv/bin/activate

2. Install the requirements:

   pip install --upgrade pip
   pip install -r requirements.txt

Once the requirements are installed, the programs will run successfully, except for the person_and_phone.py script, which requires an additional model download (details provided later).

Vision Prerequisites:

• TensorFlow > 2
• OpenCV
• scikit-learn 0.19.1 (for face spoofing; newer versions are not supported)

Audio Prerequisites:

• PyAudio
• SpeechRecognition
• NLTK

Vision Functionalities:

1. Eye Tracking:

   • Tracks eyeballs and reports if the candidate is looking left, right, or up.
   • Implemented in eye_tracker.py.

2. Mouth Opening Detection:

   • Detects if the candidate opens their mouth by recording the initial distance between lips.
   • Implemented in mouth_opening_detector.py.

3. Person Counting and Mobile Phone Detection:

   • Counts the number of people and reports if no one or more than one person is detected.
   • Detects instances of mobile phones.
   • Requires YOLOv3 model in TensorFlow 2.
   • Implemented in person_and_phone.py.

4. Head Pose Estimation:

   • Determines the direction in which the person is looking.
   • Implemented in head_pose_estimation.py.

5. Face Spoofing Detection:

   • Detects whether the face is real or a photograph/image.
   • Implemented in face_spoofing.py.

6. Face Detection:

   • Compares different face detection models; OpenCV's DNN module provides the best results.
   • Implemented in face_detector.py.

Facial Landmarks:

• Initially used Dlib's model, but switched to a TensorFlow-based model for better performance with angled faces.
• Implemented in face_landmarks.py.

Note: If you want to use Dlib models, check out the old-master branch.

Audio Functionalities:

1. Speech to Text:

   • Records audio from the microphone and converts it to text using Google's speech recognition API.
   • Runs in a separate thread to avoid disturbing the recording process.
   • Implemented in audio_part.py.

2. Text Comparison:

   • Removes stopwords from the recorded text and the question paper (in txt format).
   • Compares the contents and presents the common words and their frequencies to the proctor.
   • Implemented in audio_part.py.

Problems:

• Speech-to-text conversion may not work well for all dialects.