An internal AI facial analysis app, built by Hoop.
Facelyzer is an internal app: a mobile product that takes a single face photo and returns a reliable face-shape reading, then turns it into styling guidance. Our team built the Flutter app and the OpenCV-based analysis backend behind it in two months.
- Project Type
- Internal App
- Industry
- AI Beauty & Styling
- Timeline
- 2 Months
- Status
- Completed
Facelyzer is a facial analysis application that detects facial structure and shape using computer vision and image-processing pipelines, enabling automated analysis for styling, personalization and digital recommendations. We built the Flutter mobile app and the OpenCV-based analysis backend, designed the flow through our product design process, and delivered the core build in two months.
- Flutter frontend application for iOS and Android
- OpenCV-based analysis backend
- Image-processing pipeline for feature extraction
- Processing performance optimized for mobile usage
What shipped
Core App Screens
5
Backend Pipeline Pieces
3
Feature Areas
4
Core Build Delivered
2 Months
Computer vision that has to work on any phone
A face-shape reading is only useful if it is right, and the input is the hardest part: users photograph themselves on very different device cameras, in different light, and expect a result quickly. The brief was to make that dependable inside a mobile app rather than a lab demo.
On top of detection itself, the analysis had to plug into an ordinary mobile workflow, and the raw reading had to become something a user can act on.
Accurate face detection across device cameras
Different cameras produce very different photos, and the analysis had to stay reliable across them.
Real-time image processing performance
Image analysis is demanding, and it had to feel workable on a phone rather than a workstation.
Integrating ML pipelines into mobile workflows
The analysis pipeline had to fit naturally into an app’s upload, wait and result flow.
Turning a reading into a usable result
A detected shape needed to become ranked match percentages and styling tips a user can act on.
A Flutter app on top of an OpenCV analysis backend
We developed the Flutter frontend application to handle capture, upload and results on both platforms, and implemented an OpenCV-based analysis backend to do the demanding work. Between them sits an image-processing pipeline built for feature extraction.
Processing performance was then optimized for mobile usage, so the app stays responsive while the analysis runs. The result is a system that takes an uploaded photo and returns a face-shape reading the rest of the product builds on.
From upload to face shape, screen by screen
Five screens carry the whole experience: upload a photo, wait while the analysis runs, read the result, ask the AI chat, and see what premium adds.
01
Upload
02
Processing
03
Result
04
AI Chat
05
Premium
The feature set across four areas
The features below are the ones visible across the app's screens. The analysis core - the Flutter app, the OpenCV backend and the feature-extraction pipeline , is the part we built; everything else sits on top of it.
Analysis
- Face Image Upload With Framing Guidance
- Face Shape Detection
- Match Percentage Across 5 Shapes
- Background Processing State
Styling
- Face-Shape Styling Tips
- Hair & Beard Suggestions
- Celebrity Face Match
- Glasses Recommendations
AI Chat
- Free-Text Style Questions
- Quick Suggestion Chips
- Answers Tailored To Face Shape
Access
- Free Analysis
- Premium Tier & Get Pro Prompts
- Plan Selection & Free Trial
- Locked Tips Unlocked On Upgrade
Roadmap note: the app's home banner announces an Eye Shape Detector as coming soon.
Three pieces that make the analysis dependable
OpenCV-based analysis backend
The demanding image analysis runs in a dedicated backend built on OpenCV, keeping the mobile app light.
Feature-extraction pipeline
Every photo passes through an image-processing pipeline built to extract the facial features the shape reading depends on.
Mobile-optimized performance
Processing was tuned for mobile usage so results arrive in a way that suits how people actually use their phones.
Where the build landed
Facelyzer · internal build
Functional
AI-Powered Facial Analysis
Reliable
Image Processing Workflows
Extensible
Personalization-Driven Foundation
2 Months
Core Build Delivered
The services behind this app
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