On-device ML integration
Wired Google ML Kit into a real mobile product.
Mobile · on-device ML
On-device computer vision
Mobile developer
Not many full-stack developers ship computer vision. On mobile, I used Google ML Kit for real-time face detection to support identity verification, and object detection plus image labeling to validate user photos — all on-device.
FlutterGoogle ML KitFace detectionObject detectionImage labeling
- Runtime
- On-device
- Verification
- Real-time face detection
- Validation
- Object detection + labeling
- Platform
- Mobile
The problem
Identity and photo checks had to happen in real time, on the device, without sending raw images to a server for every frame.
What I owned
Integrated Google ML Kit on-device: real-time face detection for identity verification, and object detection plus image labeling to check that submitted photos matched what was expected.
The hard parts
- Running detection in real time on-device without degrading the experience.
- Turning noisy detection results into a clear pass or fail for users.
- Keeping image handling on-device for privacy.
The result
Shipped on-device vision features that verify identity and validate photos in real time.
How it fits together
- 01
Capture
Camera input handled on the device.
- 02
Face detection
Real-time detection to support identity verification.
- 03
Object + labeling
Detection and labeling to validate submitted photos.
- 04
Result
A clear pass or fail, without leaving the device.
Skills it proves
Real-time mobile
Kept detection responsive in the capture loop.
Privacy-aware design
Kept image processing on-device by default.
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