DETECT - Deception Tracking Through Eye Cues Technology

Faculty-advised real-time deception detection platform using gaze and facial behavior signals.

DETECT was a 9-month, faculty-advised initiative where I led a 6-member team and contributed across research, infrastructure, and modeling.

DETECT project overview slide

Highlights

  • Built a real-time AI-based deception detection system using MediaPipe FaceMesh and gaze analysis.
  • Directed full-stack development with Astro (frontend), Go (backend), and PostgreSQL.
  • Containerized with Docker and deployed on DigitalOcean VPS.
  • Engineered gaze-tracking with Savitzky-Golay filtering and affine stabilization.
  • Achieved 80%+ accuracy under the project’s evaluation setup.
  • Managed Agile execution in Jira/GitHub, including sprint planning, review, and integration.

Technical Visuals

Gaze geometry and landmark-based tracking approach

System performance dashboard from testing workflow

Application capture from DETECT frontend

Resources