Skin Analysis
GlamAR’s AI based Facial Skin Analysis brings intelligent skincare diagnostics to any digital experience. Powered by deep learning and computer vision, it evaluates skin health in real time using a single camera capture. It detects over 14 skin conditions, including acne, wrinkles, pigmentation, and pores, and returning annotated, visual results.
The analysis runs entirely through the user’s camera, no wearables or specialized hardware required, which makes it flexible to deploy in web or mobile environments at scale.
This section explains what insights it provides, how Skin Analysis works, and how it integrates into your GlamAR applications
What Insights It Provides?
| Metric / Parameter | Category | Value / Range | Annotated Visualization | Notes / Use Case |
|---|---|---|---|---|
| Skin Score | Overall Health | Integer (0–100) | ✅ | Aggregate score summarizing all measured concerns |
| Skin Type | Classification | Oily / Dry / Normal / Combination | ❌ | Based on variation across facial zones |
| Skin Tone | Classification | Fair / Light / Medium / Olive / Tan / Deep | ❌ | Melanin-based tone classification |
| Skin Age | Classification | Integer (e.g. 18 - 80) | ❌ | Estimated or perceived skin age |
| Acne | Key Condition | Integer (0 - 100) | ✅ | Detects presence of whiteheads, inflamed pimples |
| Wrinkles | Key Condition | Integer (0 - 100) | ✅ | Measures fine lines or deeper folds |
| Pores | Key Condition | Integer (0 - 100) | ✅ | Assesses visibility, density |
| Eye Bags / Under Eyes | Area-based Concern | Integer (0 - 100) | ✅ | Puffiness / shadowing in the under-eye area |
| Dark Circles | Area-based Concern | Integer (0 - 100) | ✅ | Periorbital pigmentation or discoloration |
| Pigmentation | Specific Issue | Integer (0 - 100) | ✅ | Localized melanin deposits, spots |
| Hydration | Specific Issue | Integer (0 - 100) | ✅ | Skin moisture levels / dryness |
| Redness | Specific Issue | Integer (0 - 100) | ✅ | Inflammation, irritation, flushing |
| Scars | Specific Issue | Integer (0 - 100) | ✅ | Depth, visibility of scar tissue |
| Whiteheads | Specific Issue | Integer (0 - 100) | ✅ | Closed comedones detection |
| Projected Skin (1 mo / 3 mo) | Projection / Forecast | N/A | ✅ | Forecasted skin condition under current trend |
How Does It Work?
GlamAR’s AI Facial Skin Analysis follows a streamlined process designed for speed, accuracy, and easy integration.
- Image Capture: The system accepts input from a live camera feed or an uploaded image.
- ML Inference: The captured face is analyzed using GlamAR’s trained Convolutional Neural Network (CNN) models.
- Result Generation: The model detects and scores 14+ skin metrics, generating both structured data and an annotated image output.
- Visual Feedback: The results are returned to the client as a JSON response, including all detected metrics and the processed image.
All models are optimized for real-time performance, typically completing analysis in under 3 seconds on-device or 0.5 seconds via cloud inference.
Integration Options
Choose an integration method based on your workflow and environment:
- SDKs for real-time camera capture and instant on-device analysis. Available for Web, Android, and iOS.
- APIs for backend or batch processing. Send image inputs and receive JSON results with detected metrics and annotated visuals.
- Plugins for quick, low-code setup on Shopify, Webflow, or Mirror UI modules. Ideal for non-technical teams.
- Console manages datasets, test reports, view logs, and monitor model performance.
Note:
- Use SDKs for real-time camera mode.
- Use APIs for batch or backend-triggered scans.
Setup Workflow
A typical Skin Analysis setup follows this flow:
- In the GlamAR Console, create a new Skin Analysis application. Each app generates a unique Application ID for integration and analytics tracking.
- Set up your environment (development or production), generate API keys or tokens, and configure domain whitelisting.
- Use the SDK for real-time camera capture and instant results, or the API for backend or batch image analysis.
- Run sample scans to verify detection accuracy and JSON response structure, including annotated image results.
- Integrate the workflow into your live app, kiosk, or platform. Track scan volume, performance, and diagnostic trends through the Analytics dashboard.