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Photo-based PSL estimates and explanations

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  • Why scores change
  • Face symmetry test
  • Facial ratios calculator
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Score stability

Why can the same person get a different PSL range?

Because the result describes visible relationships in one image. A different photo, a different visible state, or an updated analysis method can change the output without proving that the person changed.

  • Photo change ≠ person change
  • Match conditions before comparing
  • No validated “significant” cutoff
Use the retest protocolRead the photo guide
Diagram showing capture conditions flowing through photo measurements to a PSL result
Distance / lensPoseLightExpressionCropMethod
→

Visible relationships and ratios

→

One-photo result

≈ range

The result describes geometry in this image, not a fixed property of a person.

Separate the causes

Three kinds of change can reach the same result screen.

Before interpreting movement in a range or geometry bar, identify what changed upstream. These categories require different responses.

The photo changed

The person may be unchanged, but the two-dimensional projection or the visible pixels differ.

  • •Camera distance or lens
  • •Head turn, tilt, or camera height
  • •Light, crop, focus, or compression

Recreate the baseline capture before comparing.

The visible person changed

A real difference may be present at the time of capture, but one photo cannot tell you whether it is lasting.

  • •Expression or muscle tension
  • •Hair, glasses, makeup, or facial hair
  • •Swelling, body composition, aging, or treatment

Hold the camera conditions constant, then repeat over time.

The method changed

The product or analysis approach may have been updated since the earlier result.

  • •Different tool or product
  • •Updated measurement approach
  • •Changed result presentation

Create a fresh baseline after a meaningful product update.

What the result actually describes

The free result is a broad estimate from one photo plus four site-defined geometry relationships. It is not an identity trait, medical measurement, or population percentile.

Where variation enters

Seven variables that can move a photo-based result.

A small shift in a visible facial boundary can alter several photo proportions. The result may move even when the photograph still looks acceptable at first glance.

Distance and lens

What changes
Close wide-angle views enlarge areas nearer the lens relative to the sides of the face. Switching cameras or zoom levels also changes perspective and processing.
How to control it
Use the same device, same lens, and a marked distance. Start around 1.2 m / 4 ft rather than at arm’s length.

Pose and camera height

What changes
Yaw, pitch, and roll change the apparent widths, vertical sections, centerline, and left/right correspondence in a flat image.
How to control it
Keep lens and eyes level. Face forward with the head upright and both sides similarly visible.

Lighting and exposure

What changes
Hard shadow, glare, underexposure, or overexposure can hide edges and make the two sides of a face unequally readable.
How to control it
Face a broad soft source and keep the same placement and exposure between sessions.

Expression

What changes
Smiling, squinting, pursing lips, raising brows, or clenching the jaw changes visible facial relationships at that moment.
How to control it
Use a relaxed neutral baseline. Compare smiling photos only with the same expression.

Crop, focus, and pixels

What changes
A tiny, blurry, compressed, or partly cropped face contains less usable detail and may change where boundaries are located.
How to control it
Keep the full face in frame, use the sharp camera original, and avoid screenshots or social-media downloads.

Occlusion and editing

What changes
Hair, glare, masks, filters, beauty mode, and reshaping can cover or alter the regions used in the assessment.
How to control it
Clear measured regions and turn off digital face changes. Keep normal grooming consistent.

Analysis updates

What changes
A product update can change how the same photograph is interpreted or how the result is presented.
How to control it
Create a new baseline after a major update rather than treating the difference as a facial change.

Standardized retest

A repeat protocol you can actually reproduce.

Use this when comparing dates. The goal is not to create a flattering portrait; it is to keep the measurement conditions boring and constant.

  1. 01

    Freeze device and lens

    Use the same phone or camera and the same rear/front lens. Disable ultrawide, beauty mode, filters, and portrait reshaping.

  2. 02

    Mark the geometry

    Record camera-to-face distance, lens height, phone orientation, and where you stand. Tape marks on the floor work.

  3. 03

    Match light and background

    Use the same broad light direction and similar exposure. Avoid mixing daylight on one date with hard overhead light on another.

  4. 04

    Reset face and pose

    Rest for a moment, relax the jaw and brow, close lips gently, open eyes naturally, and face the lens without tilt.

  5. 05

    Match visibility and framing

    Keep hair, glasses, makeup, and facial hair consistent where possible. Include the same full forehead-to-chin region.

  6. 06

    Keep the original file

    Upload the camera original, not a screenshot, messaging-app copy, or edited export. Keep orientation and crop consistent.

  7. 07

    Confirm, do not cherry-pick

    Take two clear captures without moving the setup. If they disagree in pattern, call the session inconclusive and inspect the setup — do not select the higher result.

  8. 08

    Keep the analysis context consistent

    Use the same tool and create a new baseline after a meaningful product update.

Save this baseline record

A short note is more useful than relying on memory six weeks later.

Device / lens
Example: rear 1× lens
Distance
Example: floor mark at 1.2 m
Camera height
Example: lens at eye level
Light
Example: facing window, curtain closed
Expression
Neutral, lips gently closed
Notes
Record anything unusual about the session

Do not change your appearance or repeat uploads just to optimize the output. This protocol is for comparison, not score hunting.

Interpretation

How to decide whether a change deserves attention.

Use a chain of evidence, not one number. Stop at the first “no” and repeat under controlled conditions.

  1. 01

    Same tool and analysis context?

    Yes · Continue.

    No · Start a new baseline; the results are not directly comparable.

  2. 02

    Same lens, distance, height, pose, light, expression, and file quality?

    Yes · Continue.

    No · Correct the capture mismatch and retest.

  3. 03

    Does a second matched capture show the same direction and measurement pattern?

    Yes · The signal is more credible, but still photo-based.

    No · Treat the session as unstable or inconclusive.

  4. 04

    Is there a visible, independently plausible change over time?

    Yes · Describe it cautiously as a repeated photo measurement change.

    No · Do not infer that the person changed from the score alone.

Stop

Not comparable

Different lens, pose, light, crop, expression, or tool. The photo process changed first.

Check

One matched result moved

Worth repeating, but one image is not enough to separate normal capture variation from a lasting change.

Stronger signal

Repeated under matched conditions

Matched retests move in the same direction, the relevant details agree, and there is a plausible visible change.

There is no honest “±0.2 means real” rule here.

This tool has not published a validated test–retest error, smallest detectable change, or population-calibrated accuracy. Therefore no fixed score difference can prove that a person changed. A broad band crossing is a prompt to inspect the inputs and details, not a statistical significance test.

Evidence and limits

What the sources support — and what they do not.

Research supports the idea that photographs and capture conditions vary. It does not turn an informal PSL range into an objective measure of a person.

01

Different images, different impressions

Todorov and Porter found that different photographs of the same person produced large variation in social judgments. This is perception research, not a PSL validation study.

Read source
02

Distance alters photographed geometry

A 2024 study measured midface distortion at short selfie distances using both smartphones and full-frame cameras.

Read source
03

Pose and illumination are capture variables

ICAO’s technical portrait-quality report specifies frontal pose, controlled expression, visible eyes, and nearly symmetric illumination for machine-readable portraits.

Read source
04

Clear, unedited portraits improve consistency

Official photo guidance recommends a direct pose, neutral expression, even lighting, clear focus, and an original unedited image.

Read source

These references explain image variability and portrait capture. They do not validate this site’s aesthetic mapping or provide an accuracy claim.

Start with the input

Standardize the photo before you interpret the range.

Use the photo guide to create a baseline, then take the free test once and keep its limits in view.

A changed result is a question to investigate, not a verdict about you.

Open the photo guideStart the PSL test