### Senior Design: Anit-Cheat Detection system
---

#### "Measure Learning":
- Client Side Application
- Server Side Streaming
- UDP style streaming capabilites
- Camara Processing Capabilities
- Multithreading
- MIT / Free Licencing

### Behaviors:
- Bluetooch Monitoring
- Video Streaming
- Mouse Analysis
- Feature Analysis
- Privacy Concerns (FIPPA)

#### Frameworks Ideas:
- Flask
- React
- .NET MAUI

### Analysis/Training Methods:
Scope: Monitoring features to begin with, and AI enhancements to further the focus of the models and feature. Increase accurcy and integrityare of the utmost concern.

### Market Research:
- SSV
- [OpenCV](https://docs.opencv.org/3.4/index.html)
- FaceNet
- [Deepface](https://research.facebook.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/)
- [PyGaze](http://www.pygaze.org/about/)
    - this will allow for naive tracking, and will work on a computer that does not have a eyetracker
- [OGAMA](http://www.ogama.net/)
- [Tobii](http://www.tobii.com/)
- [Gaze Recorder](https://gazerecorder.com/)
- [Arsfutura](https://arsfutura.com/magazine)
- [FFMPEG](https://ffmpeg.org/)
- [EthoVision XT](https://www.noldus.com/ethovision-xt)
- [Pupil](https://pupil-labs.com/products/core/)
- [RasterVision](https://docs.rastervision.io/en/stable/usage/basics.html)

#### Systematic Possiblities:
[1. CNN-based image recognition for topology optimization](https://www.sciencedirect.com/science/article/abs/pii/S0950705120302379)
[2. Support Vector Machine (SVM) for Anomaly Detection](https://towardsdatascience.com/support-vector-machine-svm-for-anomaly-detection-73a8d676c331)

#### Medium Articles:
[1. A FaceNet-Style Approach to Facial Recognition on the Google Coral Development board](https://towardsdatascience.com/a-facenet-style-approach-to-facial-recognition-dc0944efe8d1)

#### Datasets:
[1. One-dimensional CNN for human behavior classification](https://towardsdatascience.com/one-dimensional-cnn-for-human-behavior-classification-fb4371d03633)
[2. Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10390406/)