Online Exam Student Anti-Cheat Tool

In the age of a global pandemic the entire industry has shifted to a work from home enviornment. However Student Examintions taken online are still a tricky problem to solve as there are still a lot of loop holes for students to use while giving online exams or sitting for online lectures.

The Student Anti-Cheat Tool helps reduce these problem by detecting students faces with face recognition for identification and Students onscreen time with cellphone cheating detection.

Note :- Cell phone Detection uses Yolo V4 for object detection and will impact performance.

Requirements : Create a virtual conda enviornment.

conda create --name StudentAntiCheatEnv --file requirements.txt
conda activate StudentAntiCheatEnv

Usage: 1) Place Students Face photo with their names as "Firstname Lastname.jpg" in the "Faces/" folder in project diretory for face recognition and identification.

2) Place the model file in correct folder i.e. models/yolo/yolov4.h5 Yolo V5 Model Download Link

3) Press 'Q' during rendering to abort.

4) Similar Usage for webcamDemo.py. This will render your webcam feed live with object detection and face detection for testing.

    python StudentAntiCheat.py --path "TestVideo.mp4" --name "FirstName LastName" --fps 12 --phone true --save --verbose

Output:

Requires Cuda and CuDNN along with Tensorflow GPU Else CPU inference for phone detection will be extreemely slow

TensorFlow version: 2.1.0
Eager execution: True
Keras version: 2.2.4-tf
Cuda version: 10.1
Cudnn version: 7.6
Num Physical GPUs Available:  1
Num Logical GPUs Available:  1

Note: Depending on certain Windows machines the face_recogntion library may not install correctly.

pip install cmake
pip install dlib
pip install face_recognition

If still having issue you have to build dlib with cmake.

Refrences : YoloV4 Tensorflow by https://github.com/RobotEdh/Yolov-4