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Photo quality check
The Photo Quality Check feature helps clients reduce low-quality photo submissions before photos are uploaded. It validates captured or uploaded photos and gives users clearer feedback when photo quality does not meet the configured requirements.
When to use quality checks
Use quality checks when photo quality has a direct impact on analysis accuracy, operational cost, or user trust.
Common examples include:
- Clinical studies where every submitted image must meet a consistent capture standard.
- Before-and-after treatment tracking where images need to be comparable over time.
- Remote consultations where a poor image can delay review or require manual follow-up.
- Any flow where failed analysis, manual review, or retakes are more costly than asking the user to fix the photo during submission.
What LIQA checks
Depending on the enabled mode, preset, and source, LIQA can evaluate the following signals:
- Face presence
- Enough visible skin
- Face pose / angle
- Resolution
- Blur
- Exposure
- Uneven or colored lighting
- Strong shadows
- Generic skin occlusion
- Glasses
- Forehead coverage by bangs, hat brim, or similar occlusions
Preset and source matrix
LIQA currently supports quality-check-related behavior in the following way:
| Preset | Live capture guidance | Post-capture preview validation | Notes |
|---|---|---|---|
face | Yes | Yes | Supports quality-check and occlusion-detection. |
face-180 | Yes | Yes, for front, left, and right captures | Supports quality-check and occlusion-detection across the 3-step capture flow. |
hair | Separate guided flow | Not part of the quality-check feature page | See the Presets guide for the hair-specific experience. |
For supported presets, LIQA still keeps a minimal no-face guard on uploaded images even when quality-check="off".
Configuration overview
The public configuration surface consists of:
quality-checkquality-check-sourcesignored-quality-checksocclusion-detection
For the full public API surface, see the API Reference.
quality-check
Controls whether LIQA evaluates photo quality and whether failed validation blocks submission.
For supported presets, this already includes the built-in glasses and forehead checks in both live guidance and post-capture preview validation.
| Option | When to use it |
|---|---|
off | Use when the flow is low risk, when quality validation is handled outside LIQA, or when you only need the built-in minimal no-face guard for uploads. |
normal | Use when users should see quality warnings, but the product should still allow submission after review. |
strict | Use when image quality must pass before submission, such as clinical studies, regulated workflows, or analysis flows where poor input blocks good output. |
Default: off
quality-check-sources
Limits where the quality-check policy is enforced.
| Source | Description |
|---|---|
front_camera | Applies quality-check to front-camera captures. |
back_camera | Applies quality-check to back-camera captures. |
upload | Applies quality-check to uploaded images. |
companion | Applies quality-check to images captured through the companion flow. |
Default: ["front_camera", "back_camera", "upload", "companion"]
occlusion-detection
Controls the standalone occlusion-focused checks used for glasses and forehead visibility.
Use this flag when you do not want to enable full quality-check, but still want LIQA to validate glasses and forehead visibility.
If quality-check is already enabled, LIQA already includes glasses and forehead checks in both live and post-capture validation for supported presets. In that case, occlusion-detection is only needed when you want to control occlusion validation separately from the rest of the quality-check flow.
| Option | Description |
|---|---|
off | Disables occlusion detection. |
normal | Shows occlusion-related issues but does not block submission. |
strict | Blocks submission when occlusion-related issues are detected. |
live | Uses the lightweight live occlusion pipeline during camera capture. For non-camera sources, occlusion checks still run after capture. |
Default: off
ignored-quality-checks
Excludes selected post-capture quality checks from the decision LIQA makes after capture.
Use this when you still want LIQA to measure a signal, but you do not want that signal to create a preview issue, block submission in strict mode, or appear in the emitted quality_check result.
This option does not change live guidance, live occlusion stages, or auto-capture behavior.
| Alias | Effect |
|---|---|
enough_skin | Ignores the enough-skin requirement in post-capture validation. |
pose | Ignores the post-capture face-pose requirement. |
resolution | Ignores the post-capture resolution check. |
blur | Ignores the post-capture blur check. |
occlusions | Ignores the generic occlusion signal in post-capture validation. |
glasses | Ignores the glasses detection signal in post-capture validation. |
forehead | Ignores the forehead / bangs occlusion signal in post-capture validation. |
exposure | Ignores the aggregate exposure signal in post-capture validation. |
uniform_illumination | Ignores uneven / colored lighting in post-capture validation. |
shadows | Ignores strong shadows in post-capture validation. |
Default: []
Alias mapping
The ignored-quality-checks attribute accepts short aliases, while analytics and internal contracts keep the measured quality fields under their canonical names.
| Alias | Canonical signal(s) |
|---|---|
enough_skin | has_enough_skin |
pose | has_valid_pose |
resolution | has_good_resolution |
blur | has_no_blur |
occlusions | has_no_occlusions |
glasses | has_no_eyeglasses |
forehead | has_no_hair_bangs |
exposure | has_good_exposure |
uniform_illumination | has_uniform_illumination |
shadows | has_no_shadows |
Source scoping
quality-check-sources only scopes the behavior of quality-check.
If you explicitly enable occlusion-detection, it is resolved separately from quality-check-sources.
Post-capture only
ignored-quality-checks is applied after capture during preview/postprocess evaluation only.
It does not remove the corresponding live guidance signals while the user is framing the photo.
Current UI examples
The exact preview title and issue list depend on the measured problems and the enabled validation mode.
A current strict mode preview example. LIQA shows the detected issues and keeps the submission action blocked until the blocking checks pass.
A current normal mode preview example. LIQA still lists the detected issues, but the user can submit the image after reviewing the warning.
Analytics
After post-capture validation, LIQA can emit the quality_check_result analytics event.
The event includes the quality fields LIQA measured. If ignored-quality-checks is configured, the event also includes ignored_quality_checks so you can see which checks were excluded from the decision.
Use the generated API Reference for the complete event payload contract instead of duplicating the full field list in this guide.