Face Out

2021 Β· πŸ† Honorable Mention Γ—2 β€” a web app that blurs every face in a photo or video except the one you choose.

μž₯렀상 제11회 μˆ­μ‹€ μΊ‘μŠ€ν†€λ””μžμΈ κ²½μ§„λŒ€νšŒ Β· 2021.09
μž₯렀상 AIμœ΅ν•© κ²½μ§„λŒ€νšŒ Β· 2021.11

Soongsil University Β· Capstone Design Project

What if you could blur everyone in a photo or video β€” except one person?

Face Out is a privacy-protection web app: give it one reference photo of the person to keep, then upload a group photo or video, and every other face is automatically blurred out.

View code on GitHub

Why we built it

As content creators like YouTubers exploded in number, more and more bystanders ended up on camera without consent β€” and manually blurring each one is tedious and easy to miss.

The goal: protect the image rights of bystanders, automatically β€” keep the one person who should stay, blur everyone else.

See it in action

Input β€” original clip
Output β€” everyone but the target is blurred

How it works

System architecture

From upload to download, the flow is four steps:

1
Reference
Upload one photo of the person to keep.
2
Detect & encode
Every face in the image or video is detected and turned into a face embedding.
3
Match
Each face is compared against the reference; anyone who doesn't match is flagged.
4
Blur
Flagged faces are blurred and the result is returned for download or saved to the account.
Images vs. video β€” photos and video run through separate pipelines: video is processed frame by frame on a dedicated, speed-optimized path, with a thumbnail generated for each clip.

Features

  • One-shot β€” a single reference photo is enough to exclude one person
  • Batch β€” handles multiple faces in a frame at once
  • Works on images and video, with auto-generated thumbnails
  • Fully web-based β€” nothing to install
  • Accounts β€” members save results to personal folders; guests download directly
  • Built-in Q&A board and profile management

Architecture

  • Frontend β€” HTML / CSS / JavaScript: login, upload, processing dashboard, my-page, Q&A.
  • Backend β€” Flask server with the face-recognition pipeline; Flask-SQLAlchemy over MySQL for users, media metadata, and Q&A.
  • Storage β€” separate folders for reference faces, source media, guest downloads, member outputs, and video thumbnails.

Team

Capstone team β€” Soongsil University (Mar–Nov 2021):

  • Dayoung Kil (κΈΈλ‹€μ˜) β€” frontend, backend & face recognition
  • Seoyoon Choi (μ΅œμ„œμœ€) β€” frontend concept & recognition model
  • Sunyoung Song (μ†‘μ„ μ˜) β€” frontend, backend & database

Tech stack

Flask MySQL Β· SQLAlchemy HTML / CSS / JS face-recognition