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About TinyExplorer Detection App

About the Project

The TinyExplorer Detection App is a user-friendly graphical interface designed with developmental scientists in mind. This toolbox integrates state-of-the-art open-source face recognition algorithms into an easy-to-use software package, streamlining the process of analysing facial data.

© Cardiff Babylab

Project Team

  • Concept and Project Management: Teodor Nikolov & Hana D'Souza
  • Lead Development and Implementation: Tamas Foldes
  • Code Contributions: Ziye Zhang & Teodor Nikolov

Privacy & Data Security

All processing is performed entirely on your local machine. No images, videos, or detection results are ever uploaded to external servers. Your research data stays completely private and under your control.

  • No cloud processing or data transmission
  • No internet connection required after initial model download
  • Models are downloaded once and stored locally
  • Ideal for working with sensitive research data involving human subjects

Research Applications

This application was developed to support developmental psychology research at Cardiff Babylab, enabling:

  • Automated face detection in experimental recordings
  • Batch processing of research data
  • Standardized data extraction for statistical analysis
  • Cross-platform deployment for research teams

Technical Stack

The application combines modern web technologies with machine learning frameworks:

  • Frontend: React with TypeScript
  • Desktop Framework: Electron
  • Backend: Python with Flask
  • Machine Learning: YOLO (PyTorch) and RetinaFace (TensorFlow)
  • Cross-platform: Windows, macOS, and Linux support

License

This project is released under the MIT License. See the LICENSE file for details.

Acknowledgments

Special thanks to the Cardiff Babylab team and all researchers who provided feedback and testing during development.

Contact

For questions about the application or research collaborations, please contact the Cardiff Babylab team.

Funding

This work was supported by a James S. McDonnell Foundation (JSMF) Opportunity Award and a UKRI Future Leaders Fellowship (MR/X032922/1) awarded to HD.

TinyExplorer Detection App - Advancing developmental research through innovative technology

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