30 Jul 2025 Running Face Recognition Locally in Flutter: A Practical Step Towards Offline AI

In this proof of concept, we set out to explore a simple yet powerful idea: running a real machine learning model directly inside a Flutter app. No APIs, no servers, no latency. Just fast, local processing on the device itself!

To put this to the test, we chose a straightforward and measurable use case: face recognition. Our goal wasn’t to create the most advanced facial recognition system out there, but rather to see if we could detect and identify faces in real time, entirely offline, using only Flutter and a lightweight model.

In this article, we’ll walk you through how we made it work—from building the model in Python to deploying it on-device using Google’s TensorFlow Lite (TFLite, now known as LiteRT) and ML Kit. More importantly, we’ll explore what this unlocks—not just for face recognition, but for any kind of local AI in Flutter apps. Think instant feedback, enhanced privacy, and powerful features that don’t rely on the cloud!

From Python to Phone – First Python version

The first version of the project was developed entirely in Python, using tools that helped us get up and running quickly: Haar cascades for face detection and a FaceNet model to generate face encodings. With just one profile photo per employee, the model was able to learn enough to distinguish between individuals accurately.

Everything ran smoothly in Python. Once a face was detected, it was cropped and passed through the recognition model, which generated an encoding, a unique vector of numbers representing that face. These encodings were stored in a file and later compared to new images using Euclidean distance.

But the real goal of this project was to move everything into Flutter. We didn’t want to rely on a Python server or external APIs; we wanted all the logic to run inside the mobile app, fully offline.

To do that, we had to make a few changes.

Implementation Inside A Flutter Application

First, we replaced Haar cascades with ML Kit, which works directly in Flutter and does face detection on-device. It’s reliable, fast, and handles multiple faces with ease. ML Kit gives us the bounding box for each face it detects in the camera frame.

Next, we needed to integrate the FaceNet model into our Flutter app. We converted it to TensorFlow Lite, and used the tflite_flutter package to run it directly inside the app. This package allows you to load a TFLite model, run inference on images, and get the output in Dart, all without needing any native code. The original model was over 100 megabytes, but after optimisation we managed to shrink it down to about 20 megabytes, making a huge difference in performance.

Once all the pieces were in place, the process in Flutter looked like this:

1. ML Kit detects one or more faces in the camera feed.
2. Each detected face is cropped from the frame.
3. The cropped image is passed to the TFLite model.
4. The model returns an encoding.
5. We compare this encoding to the stored encodings using Euclidean distance.
6. If it’s close enough, we know who it is.

The result is real-time face recognition, running entirely within the Flutter app: no API calls, no network latency, complete privacy, and everything processed locally on the device.

Real-Time Performance on Mobile

Once everything was set up, the results on mobile were impressively smooth. After optimising the model size, the app ran fully offline and responded almost instantly.

• On standard phones, it could process one or two faces without any noticeable lag.
• On an iPad, it could track up to 15 faces simultaneously without issues.
• No internet, no API calls, just fast, on-device results!

It was remarkable to see the entire pipeline, from detection to recognition, unfold in real time, all within a matter of milliseconds.

Additional Integration

As part of our exploration, we also integrated the system with the Microsoft Teams API. Once a face had been recognised, the app fetched real-time details about that user, such as presence status, calendar availability, and basic profile information.

Although this was just a simple proof of concept, it clearly demonstrated how facial recognition can trigger more advanced workflows, from attendance checks to smart office dashboards and access control.

It’s a small yet powerful example of how local AI can connect with business tools to deliver meaningful, context-aware interactions.

What This Unlocks & Next Steps

This project went beyond facial recognition and proved that Machine Learning (ML) models can run entirely within a Flutter app, with no need for external APIs or network connections. The results were both efficient and reliable, validating a fully on-device pipeline from model training to real-time inference.

Keeping everything local paves the way for a wide range of use cases where speed, privacy, and offline functionality are critical:

• Augmented reality integrations that respond instantly to user actions.
• Field‑based applications that work without network coverage.
• Secure environments where sensitive data must remain on the device.

Thanks to this project, we now have a solid foundation for future innovation. The next steps may include refining the recognition pipeline, exploring additional lightweight models, or extending our solution to support real‑time Augmented Reality (AR) experiences whilst maintaining privacy and performance.

If you’re interested in bringing local AI or AR capabilities into your mobile apps, we’d be happy to show you what’s possible. Whether you’re aiming to enhance user experiences, improve responsiveness, or ensure data remains securely on-device, our approach is adaptable to your specific goals. Get in touch with us today!