The model does not "understand your whole face" like a human. It converts visual patterns around eyes, nose, and mouth into numeric features, then classifies them. This guide focuses on the practical flow users can understand.
1) Input stage: photo quality matters
The model relies on visible facial patterns. Heavy filters, extreme lighting, or blocked face areas can increase misclassification.
- Use a mostly frontal photo
- Avoid strong backlight and heavy beauty filters
- Retake if mask, hands, or large accessories block key areas
2) Preprocessing: normalize data for stable inference
The uploaded image is resized and normalized to match model input format. This step improves consistency across different devices and photos.
3) Inference: feature extraction + probability scoring
The model extracts visual features and calculates class probabilities. The highest probability is shown as the main result, but close top-2/top-3 scores can indicate a borderline case.
4) Browser execution: strengths and limits
- Strength: fast response and low server dependency
- Limit: speed and smoothness vary by device/browser performance
5) Practical checklist for better reliability
- Document common failure conditions (backlight, side angle, heavy filter)
- Show a quick "retake guide" on the test page
- Optionally expose top candidates to help user interpretation
Clear communication about model strengths and weaknesses builds trust. Hiding uncertainty usually hurts both UX and content quality.