
AI face recognition allows event guests to find every photo they appear in across the entire event gallery — by simply taking a selfie. When a guest opens the face search feature, their phone camera captures a selfie, and the AI analyzes the facial features to generate a mathematical representation (a face embedding vector). This vector is compared against all detected faces in the event gallery, and within seconds, the guest sees a personal gallery containing every photo where they appear — whether they're in the foreground, the background, or part of a group shot.
For events with hundreds or thousands of photos — large weddings, corporate conferences, multi-day festivals — manually scrolling through the gallery to find your own photos is impractical. Face recognition solves this problem completely. A guest at a 200-person wedding with 2,000 photos in the gallery can find all 47 photos they appear in within 3 seconds. Without face recognition, that same guest would need to manually review every photo, which could take 30-60 minutes.
Photogala's face recognition system processes all uploaded photos automatically in the background. When a new photo is uploaded, the AI detects every face in the image, generates embedding vectors, and clusters them with matching faces. This means the face search is instant for guests — the heavy processing happens server-side during upload, not when the guest takes their selfie. The system also provides admin tools for managing face clusters: merging duplicates, labeling people by name, hiding clusters, and making manual corrections.
The guest opens the face search feature in the event gallery and takes a selfie using their phone's front camera. The selfie is captured in the browser — no app needed. For best results, the guest should face the camera directly in good lighting. The selfie is used only for matching and is not stored permanently.
The AI processes the selfie to extract a face embedding — a 128-dimensional mathematical vector that represents the unique geometry of the guest's face. This vector captures the distances between eyes, nose width, jawline shape, and dozens of other facial landmarks. The vector is a number sequence, not a visual image — it cannot be reverse-engineered back into a face.
The guest's face vector is compared against all pre-computed face vectors from every photo in the event gallery. The system calculates the similarity distance between vectors and identifies matches above a confidence threshold. This comparison happens in milliseconds, even across galleries with thousands of photos and tens of thousands of detected faces.
The guest instantly sees a personal gallery containing every photo where they appear. Photos are displayed in chronological order, and the guest can like, download, or share any of them. Group photos where the guest appears alongside others are included as well. The guest's face is highlighted or indicated in group shots so they can quickly verify the match.


The AI automatically groups detected faces into clusters — all appearances of the same person across the event gallery. Occasionally, lighting changes, angles, or accessories (sunglasses, hats) can cause the system to create two separate clusters for the same person. The merge tool lets admins combine these clusters with a single action, ensuring the person's complete photo set is unified. Merge suggestions are often provided automatically when the system detects high-similarity clusters.
Admins can assign names to face clusters. Once a cluster is labeled with a person's name (e.g., "Sarah Miller" or "Uncle Hans"), that name appears when browsing the face gallery. This makes it easy to find specific people and adds a personal touch to the gallery. Named clusters also improve the usefulness of the download feature — guests can search for people by name and download all photos containing them.
Some face clusters may represent staff, waiters, performers, or other people who don't need to appear in the guest-facing face gallery. Admins can hide specific clusters to keep the face search results clean and focused on event guests. Hidden clusters are not deleted — they can be unhidden at any time. This is particularly useful for corporate events where catering staff or AV technicians appear in many photos but are not relevant to attendees searching for their colleagues.
For edge cases where the AI's automatic clustering needs correction, admins can manually move a face detection from one cluster to another, remove incorrect face detections (e.g., a face detected in a painting or poster), or split a cluster that incorrectly merged two different people. These manual controls give admins complete authority over the face recognition results, ensuring accuracy even in challenging scenarios like identical twins or heavily made-up performers.
At a wedding with 150-300 guests and a professional photographer producing 1,000-3,000 photos, face recognition is transformative. Instead of manually scrolling through thousands of images, each guest takes a selfie and instantly sees every photo they're in — getting ready shots, ceremony moments, reception candids, and dance floor action. It's the fastest way to deliver a personalized photo experience to every guest.
Large conferences generate thousands of photos across keynotes, breakout sessions, networking areas, and social events. Attendees want to find photos of themselves with specific speakers, at specific sessions, or networking with colleagues. Face recognition makes this possible in seconds rather than hours. It's also valuable for internal communications teams who need to quickly locate photos of specific executives or team members.
At music festivals, food festivals, community events, and public gatherings, official photographers capture thousands of candid shots across multiple stages and areas. Attendees typically have no way to find themselves in these massive galleries. Face recognition gives every attendee a personal gallery — they scan a QR code, take a selfie, and instantly see every moment they were captured in. It turns a generic event gallery into a personalized memory collection.
Events spanning multiple days — corporate retreats, destination weddings, sports tournaments, training programs — accumulate enormous photo galleries. Face recognition helps participants cut through the volume to find their personal highlights across all days. A retreat participant who wants their team-building photos from day 1 and their presentation photos from day 3 can find both in seconds through a single selfie search.
Face recognition involves biometric data, which requires the highest level of data protection. Here is exactly what Photogala stores, how long, and what controls exist:
Photogala stores face embedding vectors — 128-dimensional numerical arrays — not cropped face images. A face vector is a sequence of numbers like [0.023, -0.147, 0.089, ...] that represents facial geometry mathematically. This vector cannot be reverse-engineered into a visual image of the person's face. It is useful only for comparing similarity between faces, not for reconstructing or identifying a person visually.
Face vectors are stored for the duration of the event plus a configurable retention period. After this period, all face data — vectors, cluster associations, and labels — is automatically deleted. Event admins can also manually delete all face data at any time from the admin dashboard. The retention period defaults to the same duration as the event gallery itself.
Any person can request the deletion of their face data at any time, in accordance with GDPR Article 17 (Right to Erasure). Event admins can delete individual face clusters from the admin dashboard, which removes all associated vectors and cluster data. Alternatively, guests can contact Photogala support directly for data deletion requests.
Face recognition is an opt-in feature for guests. Guests must actively choose to take a selfie for face search — the system does not identify guests passively or without their action. The selfie taken by a guest for searching is processed and compared against existing face clusters but is not permanently stored. Guest selfies are used only for the immediate search query.
Photogala's face recognition is designed with GDPR compliance from the ground up. Face vectors are classified as biometric data under GDPR Article 9 and are processed under legitimate interest (the event organizer's interest in providing a useful service to guests) with appropriate safeguards. Data processing agreements (DPAs) are available for enterprise customers. All face data is stored on EU-based servers with encryption at rest and in transit.
Face data is never shared with third parties. No advertising companies, no social media platforms, no data brokers, no government agencies have access to face vectors or recognition results. The AI model runs on Photogala's own infrastructure, not through external API services like AWS Rekognition or Google Vision. This means face data never leaves Photogala's servers during processing.
How does Photogala's face recognition compare to other solutions you might consider?
| Feature | Photogala | Alternatives |
|---|---|---|
| Face search for event guests | Selfie-based instant search across entire gallery | Google Photos: private only. Most event apps: not available. |
| Works across all guest photos | Yes — all uploads from all guests in one gallery | Google Photos: only your own library. Others: N/A |
| Admin face management tools | Merge, label, hide, manual adjust | Rarely available in event context |
| GDPR compliance | EU servers, vectors only, deletion rights, DPA available | Varies — often US-based processing |
| No app required | Browser-based selfie capture and results | Usually requires app download |
| Processing location | Own infrastructure, no third-party AI APIs | Often AWS Rekognition or Google Vision (data leaves your control) |
Selfie-based instant search across entire gallery
Google Photos: private only. Most event apps: not available.
Yes — all uploads from all guests in one gallery
Google Photos: only your own library. Others: N/A
Merge, label, hide, manual adjust
Rarely available in event context
EU servers, vectors only, deletion rights, DPA available
Varies — often US-based processing
Browser-based selfie capture and results
Usually requires app download
Own infrastructure, no third-party AI APIs
Often AWS Rekognition or Google Vision (data leaves your control)
Higher tiers unlock more powerful features and greater flexibility
Buy now, use anytime. The event duration starts after the first 10 photos are uploaded, not from the purchase date.
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Videos, comments, points and a leaderboard for guest fun.
The simple event gallery for collecting photos fast.
Uploader Definition: All guests who upload at least one photo or video count as uploaders. Unlimited guests can still view the gallery.
Fair Use Storage: Storage is sized for normal event usage. Reach out if you need unusually large galleries.
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