TinyExplorer Detection App¶

Main application interface showing file selection, model options, and confidence threshold controls

Overview¶

The TinyExplorer Detection App is a user-friendly graphical interface designed specifically for developmental psychologists working with infants and young children. This toolbox integrates state-of-the-art open-source face recognition algorithms into an easy-to-use software package, streamlining the process of analyzing facial data in developmental research.

Features¶

Simple graphical user interface for easy operation
Integration of cutting-edge face recognition models
Batch processing capabilities for efficient analysis of large datasets
Customizable confidence thresholds for detection accuracy

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

Basic Usage¶

Install a Prebuilt Release (recommended)¶

Download the latest installer for your OS from the Releases page.
Run the installer and launch the app.

Build Locally with npm (fallback)¶

If no release exists for your system or the installer doesn’t work, you can build locally.

Install dependencies:
```
npm install
```
Start in development:
```
npm run start
```
Create a distributable build:
```
npm run build
```

See also: Supported File Formats and Getting Started.

Documentation Sections¶

Copyright & Attribution¶

Project Team¶

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

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.