Restore Faces in Old Photos

Fix blurry, damaged, or low-resolution faces in old photos. AI preserves each person's identity while enhancing clarity and detail.

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How AI Face Restoration Works

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Drop a photo with blurry or damaged faces. JPG, PNG, or WebP up to 10MB.

AI Restores Faces

GFPGAN detects each face and enhances clarity while preserving identity.

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Compare before & after, then download with restored faces in full resolution.

What Face Restoration Fixes

Blurry and out-of-focus faces

Low-resolution portraits

Damaged or scratched facial areas

Motion blur on faces

Faded facial features

Group photos with multiple faces

What Face Restoration Can Fix

Our AI models are trained specifically on human faces, so they handle a wider range of facial degradation than general-purpose enhancement tools.

Blurry and Out-of-Focus Faces

When the camera focuses on the background instead of the person, or when there is not enough light for the autofocus to lock on, the result is a soft, blurry face while the rest of the image may look fine. This is one of the most common problems in casual photography, especially with older cameras and smartphones. GFPGAN reconstructs the sharp facial structure beneath the blur by referencing its deep understanding of how human faces are shaped, producing a natural result that looks like the photo was taken in focus from the start.

Pixelated Faces from Low-Resolution Photos

Photos taken with early digital cameras, webcams, or feature phones often have resolutions so low that faces are reduced to a handful of blocky pixels. Cropping into a group photo to isolate one person creates the same problem. The AI model works at the pixel level to infer what facial features should exist based on the surrounding context, generating smooth skin texture, defined eyes, and realistic hair detail even from inputs as small as 64 by 64 pixels.

Faces Degraded by Age and Physical Damage

Printed photographs degrade over decades. Chemical dyes break down, causing fading and yellowing. Physical handling introduces scratches, creases, and stains that often cut directly across faces. Water damage can dissolve sections of the emulsion entirely. For photos like these, we recommend running Photo Restoration first to repair the structural damage, then applying face restoration to sharpen and reconstruct the facial details that remain underneath the degradation.

Compressed Faces from Social Media

Every time a photo is uploaded to Facebook, Instagram, WhatsApp, or other platforms, it is recompressed to save bandwidth. Download it, re-upload it, and the compression doubles. After a few rounds, facial features become mushy and blocky, with visible JPEG artifacts destroying skin texture and eye detail. Face restoration is specifically effective here because it can distinguish compression noise from genuine facial features and rebuild clean, sharp detail without amplifying the artifacts.

Distant Faces in Group Photos

In wide-angle group shots, wedding photos taken from the back of the room, or event photography where the photographer was far from the stage, faces in the background can be tiny and indistinct. The AI detects every face regardless of size and processes each one individually. For best results with very small faces, consider using the Image Upscaler first to increase overall resolution, then run face restoration on the upscaled version for maximum facial clarity.

How GFPGAN Face Restoration Works

GFPGAN is not a generic sharpening filter. It is a neural network built from the ground up to understand and reconstruct human faces.

Traditional image sharpening tools increase edge contrast to create an illusion of detail. They work the same way on a face as they do on a tree or a building, because they have no understanding of what they are looking at. GFPGAN (Generative Facial Prior Generative Adversarial Network) takes a fundamentally different approach. It was trained on hundreds of thousands of high-resolution facial images, learning the precise geometry and texture of human faces: how eyes reflect light, how skin texture varies across the forehead versus the cheeks, how hair falls around the temples, and how the corners of the mouth form subtle creases.

When you upload a degraded photo, the model first detects every face in the image and maps 68 facial landmarks, including the outline of the jaw, the corners of each eye, the bridge of the nose, and the edges of the lips. These landmarks create a spatial reference that tells the neural network exactly where each feature should be. The model then generates a clean, high-resolution version of each face by combining what it can see in the input with its learned knowledge of facial anatomy. The result is a face that is sharper and more detailed, but still recognizably the same person.

This is what makes face restoration different from generic upscaling or sharpening. A general-purpose tool does not know that the dark blotch in the center of a blurry face is an eye. GFPGAN does. It reconstructs the iris, the pupil, the eyelashes, and the surrounding skin with anatomical accuracy because it understands the structure it is working with. The same applies to every other facial feature: the nostrils, the teeth, the eyebrows, the ear contours. The output preserves your identity — bone structure, proportions, and unique characteristics all remain intact — while replacing blur and damage with clean, natural-looking detail.

CodeFormer, the second model available on paid plans, uses a different architecture that adds a fidelity control slider. This lets you balance between maximum sharpness and maximum faithfulness to the original pixels, which is useful for severely degraded photos where you want the AI to be more conservative in its reconstruction.

When to Use Face Restore vs Other Tools

PhotoFlip offers four specialized AI tools. Here is how to decide which one to use first, depending on what is wrong with your photo.

Faces are blurry, rest of the photo is fine

Use Face Restore

If the background and objects look sharp but the faces are soft, out of focus, or pixelated, face restoration is the right starting point. The AI targets only the facial regions and leaves the rest of the image untouched, so you get the best possible result without any unnecessary processing on areas that already look good.

Photo has scratches, stains, or physical damage

Use Photo Restore first

When the entire photo is degraded with scratches, tears, water stains, or severe fading, run Photo Restoration first to repair the structural damage across the whole image. Then come back and run face restoration to sharpen the facial details. Processing in this order gives the face model a cleaner input to work with.

Entire photo is low resolution or pixelated

Use Upscale first

If the entire image is tiny or pixelated, not just the faces, run the Image Upscaler first to increase overall resolution by up to 4x. Then apply face restoration to the upscaled result for maximum facial clarity. Real-ESRGAN handles textures, backgrounds, and objects, while GFPGAN specializes in the faces.

Photo is black and white or sepia

Restore and face-fix first, then Colorize

For black-and-white photos, do all restoration and face enhancement before colorizing. The Colorize model produces more accurate and vivid colors when working with a clean, sharp input. Run restore, then face restore, then colorize as the final step.

Recommended processing order

For photos that need everything: Restore (fix damage) → Face Restore (sharpen faces) → Upscale (increase resolution) → Colorize (add color). Each step builds on the previous one. You can skip any step that does not apply to your photo.

Identity preserved

Under 20 seconds

Zero data storage

Face Restoration FAQ

We use GFPGAN and CodeFormer models that specialize in facial reconstruction. The AI detects faces, maps facial landmarks, and generates sharp, natural-looking features while preserving each person's unique identity.

No. The AI is trained to preserve identity — bone structure, eye shape, facial proportions all remain the same. It only enhances clarity and fills in damaged or blurry areas with realistic detail.

Yes. The AI detects and restores all faces in the image individually, so group photos and family portraits work well.

GFPGAN handles moderate blur well. For extremely blurry faces (where features are unrecognizable), CodeFormer with higher fidelity settings may produce better results. Both models are available on paid plans.

Yes. For best results: restore scratches and damage first, then run face restoration, then optionally colorize. Each step builds on the previous one.

No. The AI preserves your identity completely. Bone structure, eye shape, nose profile, mouth proportions, and overall facial geometry remain unchanged. The model only reconstructs detail that was lost to blur, damage, or low resolution. Think of it as cleaning a foggy window rather than reshaping what is behind it.

Yes. The AI scans the entire image and detects every face individually, regardless of how many people are in the photo. Each face is restored independently with its own identity preserved, so a group photo of ten people will have all ten faces enhanced. Faces that are already sharp are left untouched.

Face restoration works best when faces are at least roughly 64 pixels wide. For very small faces in wide-angle or landscape shots, we recommend cropping closer to the faces before processing, or using the Upscale tool first to increase the overall image resolution. This gives the face model more pixel data to work with and produces noticeably better results.

Yes. Face restoration is excellent for sharpening official documents where facial clarity matters. The AI enhances detail without altering your appearance, making it suitable for visa applications, driver's licenses, and employee badges where an old or low-quality photo needs to be improved. Always verify that the output meets the specific format requirements of the issuing authority.