About TinyExplorer FaceDetectionApp¶
About the Project¶
TinyExplorer FaceDetectionApp is a desktop application designed for developmental research, providing robust face detection capabilities using state-of-the-art YOLO and RetinaFace models. The app enables researchers to efficiently process large volumes of images and videos, extracting face detection data for analysis.
Copyright & Attribution¶
© 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
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 FaceDetectionApp - Advancing developmental research through innovative technology