# Face Photo Editing: 89-Algorithm Study; Protected-Face Background Edits Win if Qualified

Owen Harrison · September 17, 2026

> An 89-algorithm face editing study favors protected-face background edits, but a proposed 90% identity-retention threshold remains unverified.

| Takeaway | Detail |
| --- | --- |
| The retention threshold remains unverified. | 90% is a proposed identity-retention threshold; none of the supplied excerpts reports that result, a blinded identity test, or a Nepal trekking portrait evaluation. |
| Reference photos distinguish preservation from invention. | A 90% identity-retention claim requires comparison with the original person. Morphed distinguishes reference-photo editing from text-to-portrait generation, which invents a face. |
| Product recommendations are not identity measurements. | Neither Morphed’s recommendation of Nano Banana nor Pixelcut’s description of Flux Kontext Max establishes 90% identity retention in the supplied excerpts. |
| Protected-face background edits need qualification. | To establish 90% retention, an evaluation would need a defined identity-matching protocol; attractive scenery, realistic skin, and model confidence cannot substitute for that measurement. |

90% identity retention is the proposed bar, yet none of the supplied source excerpts reports a result against it. They also provide no blinded identity test or Nepal trekking portrait evaluation. That gap matters: a convincing Himalayan backdrop cannot rescue an edit that viewers attribute to someone other than the traveler. The supplied evidence likewise does not substantiate the headline’s algorithm-study claim.

The useful distinction is between preserving a photographed person and generating an appealing face. Morphed describes photo-to-portrait editing as changing elements such as background, lighting, or clothing while keeping the subject recognizable; text-only generation instead invents someone matching a description. Its recommendation of Nano Banana is a platform judgment, not a quantified identity result. Pixelcut’s description of Flux Kontext Max similarly emphasizes photorealistic portraits without supplying a retention measurement.

Protected-face background editing is therefore a candidate workflow, not an established winner. Qualification would require a defined test comparing the edited traveler with the reference photograph, with identity judgments separated from scenery and aesthetics. Until such evidence exists, 90% should remain an explicit acceptance criterion—not a synonym for attractiveness, a confidence score, or a claim that a Nepal portrait workflow has already passed.

![Quiet portrait studio with softly ivory alcove lush](https://static.mm-ais.com/article-images-ai/face-photo-editing-89-algorithm-study-pr-ai-ce85cedc.jpg)
Quiet portrait studio with softly ivory alcove lush

## Identity Conditioning

InstantID conditions a face; it does not copy one. That distinction is the entire reason the 90% blinded cutoff above exists, and it is the distinction most 2026 trekking-portrait workflows quietly skip.

According to Wang et al., “InstantID: Zero-shot Identity-Preserving Generation in Seconds” (2024), the method takes a single reference photograph and derives two separate signals from it: an identity embedding injected into the diffusion model through cross-attention, and a set of facial landmarks that steer spatial layout. Neither signal is a pixel transfer. The embedding is a compressed vector; the landmarks are a handful of keypoints. Both are strong enough to make a generated face read as the reference at a casual glance, and neither guarantees that a blinded viewer will pick the traveler out of a lineup. Commercial guides blur this line. Morphed’s June 13, 2026 guide calls Nano Banana an identity-preservation benchmark, but the supplied excerpt contains no numerical identity-match score and no blinded-test protocol — “benchmark” there is a label, not a measurement. Pixelcut likewise names Flux Kontext Max for photorealistic portraits and polished corporate headshots without publishing an identity-retention measurement.

Denoising explains the failure mode. The diffusion process reconstructs an image from noise under two pressures: the text prompt and the conditioning signals. Mountain scenery survives because it is low-frequency, prompt-anchored structure — ridgelines, sky gradients, trail geometry. Identity lives in high-frequency relationships: interocular distance, nose-to-mouth ratio, the width and angle of the jaw. Those relationships are resampled at every step, and small drifts compound. A portrait can hold Annapurna behind the traveler perfectly while shifting the eyes a couple of millimeters apart and squaring the jaw. That is conditioning a generated face, not copying the traveler’s original face pixels — and it is why photorealism and embedding cosine similarity are the wrong acceptance tests.

The evaluation task must therefore be a four-alternative identity lineup: the traveler’s separate, unedited reference portrait plus three plausible distractors matched on age, build, and complexion. Chance performance is 25%. The outcome is recorded as correct identity selection, not as a resemblance rating — “looks like him” is not a datum. Blinded viewers see neither editing-method labels nor prompting histories, and reference portraits are shot on neutral backgrounds in comparable clothing, so a trekking jacket, a lodge wall, or a Himalayan skyline cannot leak the answer.

The image-level score is correct identity selections divided by all eligible judgments. Ambiguous responses count as incorrect — no partial credit, no “close enough.” Each viewer sees only one edit of a given traveler, which reduces recognition carryover from a previously viewed frame. Reference-image handling matters at the margin: VisualGPT lists support for JPG, JPEG, PNG, and WebP references with a 16 MB upload limit, so a heavily compressed reference can starve the embedding before generation even begins.

| Signal | What it controls | What it does not establish |
| --- | --- | --- |
| Identity embedding (InstantID) | Global face-identity vector injected via cross-attention | That blinded viewers select the traveler from a 4-way lineup |
| Facial landmarks (InstantID) | Keypoint geometry: eyes, nose, mouth corners | That jaw contour and interocular spacing survive denoising |
| Denoising trajectory | Low-frequency scene structure — ridges, sky, trail | That identity-bearing feature relationships are preserved |
| Reference upload (VisualGPT) | JPG, JPEG, PNG, WebP; 16 MB cap | That a compressed reference carries enough detail for recognition |
| Blinded 4AFC lineup | Correct identity selection; chance = 25% | Nothing — this is the only measure that counts |
| Image-level score | Correct selections ÷ eligible judgments | That a passing score generalizes across travelers |

Predeclare the lineup, the distractors, and the 90% cutoff before generating a single frame. If you cannot assemble three plausible distractors, you cannot run the test — and an untested edit is a withheld edit.

![Minimal photography pavilion with sheltered neutral interior rainwashed](https://static.mm-ais.com/article-images-ai/face-photo-editing-89-algorithm-study-pr-ai-954cdf36.jpg)
Minimal photography pavilion with sheltered neutral interior rainwashed

## Benchmark Evidence

A traveler wants to improve the background of an existing trekking portrait without changing their face. The practical choice is a photo-to-portrait edit, not text-to-portrait generation, which invents a person rather than preserves an existing identity. For this worked example, the traveler selects Nano Banana based on Morphed’s identity-preservation recommendation. Morphed quotes approximately 3–4 credits per portrait, but provides no cash conversion; that is a credit budget, not a verified dollar price.

The alternatives have different documented advantages. Pixelcut supports uploaded photographs, identifies Flux Kontext Max as a photorealistic portrait option, and advertises a free trial with watermark-free outputs. VisualGPT accepts JPG, JPEG, PNG, and WebP files up to 16 MB and requires one uploaded photograph. However, its described workflow automatically enhances facial details alongside lighting, colors, and background—a reason to inspect the face carefully when likeness matters more than beautification.

The decision is to request a background-only edit and accept the result only after comparing the face with the original. This is a conditional choice, not proof that protected-face editing wins: the excerpts establish neither face-locking controls nor a 90% identity-match result. They also do not substantiate an 89-algorithm study or a Nepal trekking evaluation. If facial features change, keep the original rather than treating a more attractive portrait as an identity-preservation success.

Deng et al.'s ArcFace paper reports 99.83% verification accuracy on Labeled Faces in the Wild, and that number gets quoted in current editing discussions as though it guaranteed anything about an edited portrait. It does not. LFW is a same-or-different pair-verification benchmark: the model decides whether two unedited photographs depict the same person. The 99.83% describes a recognition model's decision boundary on that dataset — not a human pass rate, and not a measurement of any generator's output. A high similarity score is machine verification; it is not evidence that a viewer recognizes the traveler.

The pose condition matters more than the headline. According to the same paper, ArcFace reaches 98.27% on CFP-FP, a frontal-to-profile benchmark built specifically to test whether a model can match a frontal face against a profile view. That is the closest published analogue to a trekker photographed mid-stride, head turned toward a summit ridge, one cheek in shadow. But CFP-FP still uses real photographs of real faces under controlled capture. It does not test whether a synthetic edit that rotated, relit, or reconstructed that profile preserved the traveler's identity. Frontal-face performance is not transferable evidence for a three-quarter or profile trekking frame, which is exactly the frame most summit shots produce.

AgeDB-30 adds a third axis: 98.15% on cross-age verification, where the paired images differ by age. Aging is a natural, gradual transformation of a real face. A generative edit can change facial geometry — jawline, nose width, eye spacing — in ways no aging process produces. Passing AgeDB-30 says the model tolerates one kind of within-person variation; it says nothing about tolerance for synthetic geometric drift, which is the failure mode that makes an edited trekking portrait read as a different person.

All three figures belong to the paper's recognition-model evaluation. ArcFace is a loss function and a trained embedder, not an image generator. The experiment this guide actually needs — independently collected human identity judgments on Nepal trekking portrait edits, blinded and scored against a reference portrait — does not appear in that paper and, in the sources reviewed here, has not been published for these workflows. That absence is why the cutoff above is a human-match criterion rather than a similarity threshold.

| Benchmark | Reported accuracy | Condition tested | What it does not establish |
| --- | --- | --- | --- |
| LFW | 99.83% | Same/different pair verification, frontal | Human match rate on edited portraits |
| CFP-FP | 98.27% | Frontal-to-profile matching | Identity retention in synthetic profile edits |
| AgeDB-30 | 98.15% | Cross-age verification | Robustness to synthetic geometry change |

Label these figures as historical benchmark context. They are not a newly measured Nepal success rate, and they cannot rank editing workflows by identity retention. A platform recommendation is not a quantified result: Morphed's stated verification recommends Nano Banana for identity preservation, but that is a recommendation, not a measured pass rate. The Perfect Corp source title claims 14 AI portrait generators were tested, yet the fetched content contains page styling rather than the article's methods or results. Neither supports a ranking.

Before you rank any workflow by identity retention, require a directly comparable blinded portrait result: independent human judges, a reference portrait, and a reported match rate. Until that exists for a given tool, treat every ArcFace figure as context about a recognition model — and withhold the ranking.

![Benchmark Evidence — Face Photo Editing](https://static.mm-ais.com/article-images-pixabay/face-photo-editing-89-algorithm-study-pr-746d8f5f.jpg)

## Editing-Method Comparison

Protected-face background editing is the explicit winner among qualifying outputs, not an automatic identity pass. Its advantage is methodological: it changes fewer identity-bearing variables than localized generative face repair or full-portrait regeneration. This guide has not established numerical superiority in Nepal-specific trials. The preference applies only after an output meets the predeclared blinded human identity-match cutoff above; reject or withhold below-cutoff and untested images, regardless of the workflow’s reputation.

Compare the workflows using the same traveler and source photograph, with output dimensions and evaluation presentation held constant. Keep the reference portrait, displayed portrait scale, crop, and viewing conditions consistent across candidates; conceal workflow labels from viewers. Otherwise, a larger face or more favorable presentation could masquerade as an editing-method advantage. Background-only editing must actually preserve the protected facial region, rather than merely instructing a generator to “keep the face unchanged.” The relevant distinction is what pixels the process replaces, not what its prompt promises.

According to VisualGPT, its portrait workflow takes one uploaded photograph and automatically enhances lighting, colors, background, and facial details. That description illustrates why “portrait enhancement” is not a sufficiently precise workflow category: background modification and facial modification can occur together. For this comparison, classify each candidate by its actual treatment of the face. A nominal background edit that also synthesizes facial details belongs outside the protected-face category. Neither photorealism nor a high face-embedding similarity score can substitute for the required blinded identity result.

The protected-face method’s characteristic failure is an interface problem: an unchanged face can sit awkwardly against a newly generated Himalayan background. Mountain illumination may imply a light direction or color inconsistent with the original portrait, while the protected boundary can produce visible seams. Localized repair may address an objectionable transition but also regenerate an eye, skin feature, or mouth contour. Treat that repair as a different candidate requiring evaluation, not as a cosmetic adjustment that inherits the earlier candidate’s qualification.

Preregister the tie-break order before reviewing qualifying outputs: least facial regeneration first, fewest compositing artifacts second, and strongest preservation of the original trekking setting third. Apply that order sequentially rather than averaging the criteria into an aesthetic score. A prettier regenerated face does not outrank a qualifying protected-face edit merely because its lighting is smoother. If the protected-face candidate fails the identity cutoff, however, its methodological preference cannot rescue it.

Identity retention and travel authenticity remain separate judgments. Replacing an ordinary Nepal hillside with an invented Everest viewpoint must be disclosed as a creative composite even when viewers recognize the traveler. Before editing, record the protected region, fixed presentation conditions, and tie-break order; use the following decisions only after identity qualification.

| Workflow | Identity-bearing pixels regenerated? | Main Nepal-portrait risk | Decision |
| --- | --- | --- | --- |
| Protected-face background edit | No, within the protected region | Seams or mismatched mountain lighting | Explicit winner among qualifying outputs |
| Localized generative face repair | Some | Altered eyes, skin features, or mouth geometry | Conditional alternative |
| Full-portrait regeneration | Usually most or all | A convincing but different traveler | Last choice; no exemption from testing |

![Editing-Method Comparison — Face Photo Editing](https://static.mm-ais.com/article-images-pixabay/face-photo-editing-89-algorithm-study-pr-9dfd8a06.jpg)

## What the Data Doesn't Tell You

According to NISTIR 8311 (2020), the National Institute of Standards and Technology evaluated 89 pre-pandemic face-recognition algorithms and found error rates ranging from roughly 5% to 50% when matching digitally masked faces to unmasked reference photographs. That spread is the most useful counter-evidence in this workflow, and it is routinely misread. NISTIR 8311 measured machine matchers against synthetic masks — not human viewers, not trekkers, and not the specific occlusion geometry of a Khumbu midday. Read it as directional evidence that covering identity-bearing regions degrades matching, and degrades it unevenly across systems. Do not read it as a human recognition estimate for goggles and scarves.

The transfer problem is geometric, not statistical. Snow goggles remove the periocular band that carries much of the signal human viewers use; a scarf pulled to the nose removes the lower face; helmet straps cut diagonal lines across the cheek and jaw; hard midday sun at altitude flattens the nasolabial folds and eye sockets into a single shadow. These are four different visibility conditions, and an edit that succeeds on an unobstructed Nepal portrait — full face, soft light, no gear — licenses nothing about an edit in which those regions disappear. Treat occlusion class as a separate stratum: an unobstructed pass is evidence for unobstructed edits only.

Viewer population is the second blind spot. Recognition by close companions and recognition by unfamiliar observers are different tasks with different difficulty. A companion can identify a friend from gait, jacket, or context; a stranger must rely on the face alone. A panel of unfamiliar observers therefore cannot establish how friends will respond to the portrait, and a panel of friends cannot establish the stranger-level result. State which population you tested, and do not let one stand in for the other.

Selection bias compounds this. If the same viewers who ranked candidate generations also served on the retention panel, they are judging images they already chose — a preference-recognition entanglement that inflates apparent match rates. Reserve a fresh observer pool, naive to the generation process, for the final retention decision. The selection panel and the evaluation panel should not overlap.

Finally, an observed sample pass is not a population guarantee. A batch of viewers clearing the predeclared cutoff above is a point estimate, not a floor for every future viewer. Report uncertainty — the interval around the observed rate, the panel size, and the occlusion stratum — rather than presenting the cutoff as a proven minimum. Photorealism and a high face-embedding similarity score still do not establish preserved identity; they are inputs, not verdicts.

| Evidence source | What it actually measures | What it cannot license |
| --- | --- | --- |
| NISTIR 8311 (2020), 89 algorithms | Machine matching of masked to unmasked faces; error rates roughly 5%–50% | Human recognition rates under goggle or scarf occlusion |
| Unobstructed Nepal portrait pass | Full-face visibility in soft light | Edits where goggles, scarf, or straps remove key regions |
| Unfamiliar-observer panel | Stranger-level identification from the face alone | How close companions will respond to the portrait |
| Viewers who also picked the edit | Preference and recognition, entangled | An unbiased retention decision |
| Single-batch sample pass | One observed group at one moment | A minimum recognition rate for every future viewer |

Concrete next action: before your next retention run, write down three things — the occlusion stratum, the observer population, and whether the panel is fresh. If any of the three is unstated, the image is untested, and untested images are withheld, not kept.

![What the Data Doesn't Tell You — Face Photo Editing](https://static.mm-ais.com/article-images-pixabay/face-photo-editing-89-algorithm-study-pr-0e843d93.jpg)

## Worked Nepal Case

According to Schroff, Kalenichenko, and Philbin’s “FaceNet: A Unified Embedding for Face Recognition and Clustering,” published at CVPR 2015, the system achieved 99.63% verification accuracy on the standard 6,000-pair Labeled Faces in the Wild evaluation. Those are published research figures, not measurements of an edited Nepal portrait. Their useful role here is to anchor a calculation—not to qualify a traveler’s image. The distinction is between an algorithm’s performance on a verification benchmark and viewers’ ability to recognize a particular person after editing.

Converting that reported accuracy into an illustrative benchmark-scale error count gives 6,000 × (1 − 0.9963) = 22.2 errors, or approximately 22. This is arithmetic based on the paper’s reported accuracy, not an independently reported integer error tally. The fractional result should remain visible because it exposes the calculation’s status: it translates a published percentage into an approximate count rather than reconstructing the underlying evaluation outcomes. It supplies no error estimate for an Annapurna Base Camp portrait.

Now consider an explicitly hypothetical evaluation of one AI-edited Annapurna Base Camp portrait. Before collecting judgments, the evaluator declares the article’s blinded human identity-match cutoff as the retention criterion. Then 100 independent eligible viewers assess the edited image under blinded conditions, attempting to match it to the traveler’s reference portrait. Suppose 94 identify the traveler correctly. The observed human identity-match score is 94 ÷ 100 = 94%. These observations are invented solely to demonstrate the decision procedure; they are not results from a conducted Nepal evaluation.

To quantify sampling uncertainty, apply the score-interval method described by Edwin B. Wilson in his 1927 paper, “Probable Inference, the Law of Succession, and Statistical Inference.” For a two-sided 95% interval, use z = 1.96. With p̂ denoting the observed proportion and n the viewer count, the Wilson limits are [p̂ + z²/(2n) ± z√(p̂(1 − p̂)/n + z²/(4n²))] ÷ [1 + z²/n]. Substituting the hypothetical observations yields approximately 87.5%–97.2%. The statistical method comes from published research; the viewer responses supplied to it remain demonstration inputs.

Retain this hypothetical image under the predeclared observed-score rule, and report its interval alongside the decision. The interval’s lower endpoint falls below the cutoff, but requiring that endpoint to clear the cutoff would substitute a different acceptance rule after evaluation. Passing on the observed score does not prove that the population-level identity-match rate meets a minimum. Neither does FaceNet’s benchmark result.

For an actual Annapurna image, withhold qualification until its own blinded judgments are collected; reject a below-cutoff result. Photorealistic rendering and high face-embedding similarity cannot fill an empty human-evaluation record. The actionable distinction is to keep the retention decision and its uncertainty together without treating either benchmark accuracy or a successful sample as a population guarantee.

![Worked Nepal Case — Face Photo Editing](https://static.mm-ais.com/article-images-pixabay/face-photo-editing-89-algorithm-study-pr-5d8ed7d3.jpg)

## How to Choose Well

A Nepal portrait that has never been through a blinded match test is not a weak candidate — it is not a candidate. The gate is eligibility, not aesthetics. If the final exported portrait has no eligible blinded evaluation, withhold it from the identity-verified set, no matter how convincing the Himalayan scenery behind it looks. The mechanism is simple: scenery carries no identity signal. A flawless Annapurna ridge tells you nothing about whether a viewer can match the face to the traveler's reference portrait. Pixelcut supports portrait creation from uploaded photographs as well as from text prompts (Pixelcut), which means the same class of tool can synthesize a plausible face with no reference photograph at all. That is exactly why a beautiful background cannot be promoted into evidence of identity.

If an image falls below the predeclared cutoff, reject that version. Do not round the score upward, and do not swap in an automated similarity score as a substitute. Embedding similarity is a distance in a learned metric space; it is not a human match rate, and the two can diverge sharply on the same pair of faces. Rounding upward is a decision to change the rule after seeing the data — a post hoc threshold, which is the one thing a predeclared cutoff exists to prevent. Re-render, and treat the result as a fresh candidate.

When several edits qualify, apply the predetermined workflow tie-break rather than choosing whichever image makes the traveler look most conventionally attractive. Attractiveness is a confound, not a criterion: it shifts where viewers fixate and can move match rates in either direction. A defensible tie-break is declared before rendering — for example, highest observed match rate first, then fewest identity-bearing edits, then earliest render. If your workflow has no declared tie-break, you do not have a tie-break; you have a preference.

If a passing portrait is subsequently face-restored, upscaled, denoised, relit, or otherwise changed in facial detail, treat the resulting file as a new candidate and evaluate that deliverable before retaining it. The earlier evaluation described a different file. Restoration and upscaling routinely alter pore structure, eye geometry, and jaw edges — precisely the features viewers use to match a face. Budget for the re-render: Morphed's June 2026 guide quotes approximately 3–4 credits per portrait, and the supplied excerpt gives no cash conversion for those credits, so confirm the conversion yourself before committing a batch.

When you report a retained Nepal portrait, state its observed score, judgment count, uncertainty interval, and viewer population, and describe the result as sample-tested rather than universally identity-preserving. A match rate from a finite panel carries binomial uncertainty; a small panel yields a wide interval that can straddle the cutoff, and a panel of strangers is a different task from a panel of people who already know the traveler. "Sample-tested" is the honest claim. "Identity-preserving" is not.

| Condition | Action | Why it holds |
| --- | --- | --- |
| No eligible blinded evaluation on the final export | Withhold from the identity-verified set | Pixelcut can generate faces from prompts alone, so scenery is not identity evidence |
| Observed match rate below the predeclared cutoff | Reject the version; no rounding, no embedding-score substitution | Human match rate and embedding distance are different measurements |
| Two or more versions at or above the cutoff | Apply the predeclared tie-break | Attractiveness is a confound that moves viewer attention |
| Passing file later face-restored or upscaled | Treat as a new candidate; re-evaluate before retaining | Facial detail changed, so the prior evaluation is void |
| Re-render required for a new candidate | Budget roughly 3–4 credits per portrait (Morphed, June 2026); verify cash conversion | The excerpt gives no cash conversion for those credits |
| Reporting a retained portrait | State score, judgment count, uncertainty interval, viewer population | Finite panels yield intervals that can straddle the cutoff |

## What to do next

| Step | Action | Why it matters |
| --- | --- | --- |
| 1 | Before editing any Nepal trekking portrait, write down the acceptance criterion as a 90% blinded human identity-match cutoff against the traveler's original reference photograph — not against the edited image itself. | A 90% identity-retention claim is meaningless without comparison to the original person; the cutoff must be predeclared, not chosen after seeing results. |
| 2 | Define the identity-matching protocol in advance: who judges, how many blinded viewers, what they see (edited traveler vs. reference photo, with scenery and lighting hidden or controlled), and how agreement is scored. | None of the supplied excerpts reports a blinded identity test or a Nepal trekking portrait evaluation, so the protocol has to be built rather than borrowed. |
| 3 | Separate identity judgments from aesthetics: instruct judges to ignore the Himalayan backdrop, skin realism, and model confidence scores when deciding whether the edited face is the same traveler. | Attractive scenery and realistic skin cannot substitute for the measurement; a convincing backdrop cannot rescue an edit viewers attribute to someone else. |
| 4 | Treat Morphed's recommendation of Nano Banana and Pixelcut's description of Flux Kontext Max as platform judgments about photo-to-portrait editing, not as quantified identity-retention results. | Product recommendations are not identity measurements, and neither excerpt establishes 90% retention. |
| 5 | When using an InstantID-style workflow, verify that the reference photo is conditioning the face through an identity embedding and facial landmarks rather than transferring pixels — and that the output is still compared back to the reference photograph. | InstantID conditions a face; it does not copy one. That distinction is exactly why the 90% blinded cutoff exists, and it is the step most 2026 trekking-portrait workflows skip. |
| 6 | Keep only edits that meet the predeclared 90% cutoff; reject or withhold every untested image and every image below the cutoff, and label protected-face background edits as a candidate workflow rather than an established winner. | Until a defined test exists, 90% stays an explicit acceptance criterion — not a synonym for attractiveness, a confidence score, or proof that a Nepal portrait workflow has passed. |

## Frequently Asked Questions

**Has any of the supplied evidence shown that a face edit retains 90% of the person's identity?**

90% is a proposed identity-retention threshold, and none of the supplied excerpts reports a result against it, a blinded identity test, or a Nepal trekking portrait evaluation.

**Does the evidence actually support the headline's 89-algorithm study claim?**

The supplied excerpts do not substantiate an 89-algorithm study.

**How would I test whether people still recognize me in an edited trekking portrait?**

The proposed test uses a blinded four-alternative identity lineup with a separate, unedited reference portrait and three plausible distractors matched on age, build, and complexion, giving chance performance of 25%.

**Do uncertain identity matches count toward the 90% cutoff?**

The image-level score is correct identity selections divided by all eligible judgments, with ambiguous responses counted as incorrect and no partial credit.

**Does InstantID copy my actual face pixels into the edited photo?**

InstantID derives an identity embedding and facial landmarks from a single reference photograph, but neither signal transfers the original face pixels or guarantees recognition by blinded viewers.

**What should I do if a background-only edit also changes my facial features?**

If facial features change, keep the original rather than treating a more attractive portrait as an identity-preservation success.

## Quick answers

| What is the proposed identity-retention threshold mentioned in the article? | 90% is a proposed identity-retention threshold. |
| --- | --- |
| How does InstantID condition a face according to Wang et al.? | InstantID takes a single reference photograph and derives two separate signals from it: an identity embedding injected into the diffusion model through cross-attention, and a set of facial landmarks that steer spatial layout. |
| Why does the diffusion process cause identity failure modes in generated portraits? | Identity lives in high-frequency relationships such as interocular distance and jaw angle, which are resampled at every denoising step, causing small drifts to compound while low-frequency scene structures survive. |
| What specific evaluation task is required to measure identity retention? | The evaluation task must be a four-alternative identity lineup consisting of the traveler’s unedited reference portrait plus three plausible distractors matched on age, build, and complexion. |
| How is the image-level score calculated in the proposed evaluation? | The image-level score is calculated as correct identity selections divided by all eligible judgments. |

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