Before using an AI-generated headshot, check that it represents your current appearance and does not imply a real setting, role, or event that never existed. A portrait can look convincingly photographic while getting important details wrong. For a profile meant to introduce you, ask whether someone could reasonably connect the image with the person they will meet.
The practical review has three parts: recognizable likeness, what the scene communicates, and what you know about the file's creation. Keep those separate from whether the result is flattering. This guide offers an editorial checklist for reviewing your own portrait, not a validated identity test or a method for detecting every synthetic image.
Realistic, recognizable, and actually photographed are different
A realistic image looks like something a camera could have captured. A recognizable image resembles a particular person. A photograph of an actual moment records something that happened in front of a camera. A generated portrait may satisfy the first two descriptions without satisfying the third.
That distinction changes the review question. You already know that your headshot was generated, so searching only for strange fingers or obvious rendering defects misses the main task. An anatomically plausible image can still show a different hairline, a changed smile, unfamiliar eyewear, or an invented workplace. Absence of an obvious defect does not resolve those questions.
A stylized avatar and a professional introduction also have different purposes. Decide which you need before selecting an image. If a recipient expects a straightforward current portrait, a recent photograph is a useful alternative when you cannot confidently explain what the generated version changed.
What research on familiar synthetic faces actually shows
Kramer and colleagues' 2025 study examined generated images of fictional and celebrity identities. In Experiment 2, 115 participants judged real versus synthetic celebrity images; mean accuracy was 52%, not significantly different from 50% chance. These were controlled image-classification tasks, not evaluations of commercial headshot services. See the familiar-face study, DOI: 10.1186/s41235-025-00683-w.
The researchers matched image characteristics and adjusted general properties of synthetic images. Later experiments found some benefit from reference photographs, with limits depending on the task; familiarity offered only modest benefits overall. The paper did not establish that every generated portrait faithfully preserves a private individual's appearance, and it did not test whether a portrait improves hiring outcomes.
The useful distinction is between believing an image is a photograph and deciding whether it is a suitable representation of you. Neither a stranger's confident “looks real” nor a familiar person's approval is a forensic certificate. A likeness review can help you notice unwanted changes without determining the image's origin from its pixels.
Polish is not a reliable authenticity signal
Miller and colleagues' 2023 experiments found that White AI-generated faces could be judged human more often than real human faces. The first experiment involved 124 White adults living in the United States; the paper also reanalyzed earlier data. The authors call this AI hyperrealism. The research record and linked paper describe the methods and demographic scope, DOI: 10.1177/09567976231207095.
That finding concerns judgments of particular face images and a particular generation method. It is not a universal result for every skin tone, generator, or portrait. It does not mean a smoother-looking face is more authentic, or that any demographic group has a more suitable appearance. Use it as a reason to avoid equating convincing polish with accurate representation.
Review likeness against a small set of current photographs
Start with a few recent photographs you know came from ordinary camera captures. Choose views that show your face clearly, including your usual smile if the generated headshot is smiling. Use these as practical references, not a single perfect template: real photos vary with angle, lighting, and expression.
Keep the candidate and references at comparable display sizes. Look for differences you can name, rather than trying to assign an identity percentage. This is a review of whether you want to present this image as yourself; it is not a measurement of facial accuracy.
- Check the overall facial outline. Does the candidate introduce a consistently narrower jaw, a different cheek shape, or a chin you do not recognize across your references? Avoid judging one isolated difference that could arise from pose. If the whole result feels like a similar-looking stranger, set it aside.
- Inspect distinctive features. Compare your hairline, brows, nose, lip shape, and any details you consider part of your recognizable appearance. You do not have to preserve every temporary blemish, but distinguish a deliberate cosmetic choice from a change you never requested.
- Check the expression and visible teeth. If the portrait shows a broad smile, compare it with a real smiling reference. Look for a smile or tooth arrangement that does not resemble yours. When no suitable reference is available, mark that part uncertain rather than assume plausibility means accuracy.
- Review everyday styling. Are the glasses, facial hair, hairstyle, and apparent age consistent with the introduction you want to make now? An imagined future haircut can be an interesting illustration while being a poor description of your present appearance.
- Inspect the final crop. Check both the saved file at a useful viewing size and the small thumbnail people will actually see. A crop may hide a feature needed for recognition or make an otherwise acceptable portrait unsuitable for the profile.
If you ask someone who knows you for feedback, ask specific questions: “Which details differ from how I look now?” and “Would anything here surprise someone meeting me?” Avoid treating agreement as proof of authenticity. Our profile-photo feedback guide explains how to ask about a picture without revealing your favorite first.
Check what the background and clothing imply
A headshot communicates more than a face. An invented conference backdrop can suggest attendance; a company logo can suggest an affiliation; a uniform or credential badge can suggest a role. These are hypothetical interpretation risks, not claims that a particular service routinely creates them.
Imagine a good likeness in a generated laboratory scene with a badge you do not hold. The likeness question may be resolved while the scene still gives a misleading introduction. A simpler background removes that particular implication without requiring a verdict about the person's face.
Review visible text, logos, badges, jewelry, and objects before publishing. Decide whether each detail belongs in the image and whether the accompanying caption explains the situation accurately. For formal identification or another use with specific photo requirements, follow the recipient's instructions and use the required type of photograph.
Use provenance information for the question it can answer
Content Credentials can record aspects of how an image was created or edited. The C2PA explainer describes this as provenance and explains that provenance alone cannot establish whether the depicted content is true, accurate, or factual. Adoption is optional; absence of a credential is not by itself evidence that an image is fake.
If your tool or viewing application exposes that information, read what it actually says about the particular file. A recorded generation step can clarify its origin. It cannot establish that the pictured jawline matches yours or that an invented office exists. Likewise, a credential describing a camera origin is not a universal certificate for every claim someone makes about the scene.
Keep the source photographs, selected output, and any editing notes you need privately. If you export another copy or change the crop, review that version too. For an existing photograph altered with beauty effects, our photo-processing audit covers a different question: which edits changed the displayed picture.
Make a clear decision before you publish
- Use the image when its likeness, scene, and intended description are satisfactory for the purpose, and the destination's requirements allow that type of image. Describe it as AI-generated or AI-assisted where that information is relevant; do not call it a studio session that never occurred.
- Revise or replace it when you can identify a specific mismatch, such as invented eyewear, an unfamiliar smile, or an inappropriate badge. Recheck the full result after any revision rather than assume only the requested detail changed.
- Choose a current photograph when important details remain uncertain or the task requires a camera capture. Our baseline photo guide provides a starting point for a clear face photo without inventing a new setting.
This decision does not require choosing between “perfect” and “unprofessional.” A usable introduction can retain normal texture, asymmetry, and everyday styling. Define the practical purpose before generating or reviewing more versions, so the exercise does not become an endless effort to replace your appearance with an idealized template.
Where FaceScore feedback fits
FaceScore provides photo-based appearance feedback. If you want to explore it, use a clear current image in the FaceScore app. We operate the app and this publication. Feedback on a generated portrait concerns the supplied representation; it does not verify identity, certify a camera capture, or demonstrate a physical improvement.
Keep model-dependent scores separate from your decision about honest representation and from your value as a person. The useful result of this checklist is a portrait whose likeness, scene, and description you can explain comfortably, with uncertainties resolved or a different image selected.