Face Tools
Age Guesser
Estimate how old you look from a face photo with AI age range and confidence.
Attractiveness Test
Upload a portrait to get an AI attractiveness score with confidence bands and interpretation table.
Eye Shape Detector
Detect eye shape, canthal tilt, and eye color from a portrait with AI.
Eyebrow Type Detector
Upload a portrait to detect eyebrow type with confidence and grooming interpretation.
Face Shape Detector
Upload a portrait to detect your face shape with AI and confidence scoring.
Face Symmetry Test
Upload a face photo to estimate symmetry score and compare mirrored facial halves.
Golden Face Ratio Analyzer
Upload a portrait for AI golden-ratio facial analysis and use the on-page manual calculator.
Hair Color Detector
Detect hair color, undertone, and depth from a portrait with AI confidence scoring.
Nose Shape Detector
Detect nose shape, bridge profile, and tip direction from a portrait with AI.
Skin Type Detector
Detect likely skin type from a portrait with AI confidence and care-direction context.
How To Use Face Tools Effectively
This category currently includes 10 tools. Choose one baseline tool first, then add one or two supporting tools for cross-checking.
Face tools are visual inference tools. They help with structured appearance analysis such as shape, symmetry, apparent age, and feature classification, but they are not medical or diagnostic systems.
Output quality depends heavily on photo quality. For best results, use neutral lighting, a clear frontal angle, and consistent framing when comparing results over time.
What This Category Is Best For
- Feature classification: Use Face Shape, Eye Shape, Eyebrow, Nose, and Hair tools for trait-level analysis.
- Appearance benchmarking: Use Face Symmetry and Golden Ratio outputs for structured visual comparison.
- Consistency over time: Retest with matched photo conditions before drawing conclusions.
Input Quality Checklist
- Use evenly lit photos with no harsh shadowing or strong filters.
- Keep camera angle and distance consistent across retests.
- Use neutral expression for classification-focused checks.
Interpretation Notes
- Confidence and class output should be interpreted together.
- Differences across photos often reflect angle/lighting changes.
- Use results for directional insight, not fixed labels.
Common Mistakes To Avoid
- Comparing outputs from very different image conditions.
- Treating visual classification as health or identity diagnosis.
- Using one result snapshot as a permanent assessment.
Recommended Starting Tool: Face Shape Detector
Start here for a broad baseline before drilling into individual feature tools.