assisted_self-portrait

video performance, ar filter, 16:9, hd, sound, 1:30 minutes, 2014–2026

creating a self-portrait using images from the web, with the aid of an anonymous search engine composed in a screen-performance on my computer desktop.

how it works?

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flowchart TD
  START[START] --> WEBCAM[CREATE WEBCAM]
  START --> SEARCH[SEARCH IMAGES]

  WEBCAM --> TRACK[MEDIAPIPE FACE MESH]
  TRACK --> LANDMARKS[EXTRACT LANDMARK POSITIONS]

  SEARCH --> PROXY[PROXY IMAGE]
  PROXY --> DETECT[DETECT FACE]
  DETECT --> CROP[CROP FACE REGION]

  LANDMARKS --> POSITION[POSITION OVERLAY WINDOWS]
  CROP --> POSITION

  POSITION --> COMPOSITE[COMPOSITE ON WEBCAM]

  LANDMARKS -.-> TRACK
  COMPOSITE -.-> TRACK

Loading

bundled default images are shown instantly while the search + crop pipeline runs in the background. each detected face gets its own unique set of portrait images.

features

• instant start: bundled face-part images display immediately — no loading screen
• multi-face: each detected face (up to 5) gets its own unique set of portrait images
• face centering: webcam auto-zooms and centers on the detected face
• screenshot: capture the ar view with overlays + "le" watermark (desktop download / mobile share)
• emoji loader: cycling 👁👂👃👄 indicator while images load in background
• animated favicon: matching emoji animation in the browser tab
• corner ui: reload, info panel, "le" glitch signature, camera — inspired by leandroestrella.com

tech stack

• face tracking: mediapipe face mesh (468 landmarks, up to 5 faces)
• image cropping: face-api.js (ssd mobilenet + 68-point landmarks)
• image search: duckduckgo image search (server-side proxy)
• server: express (local dev) / php + apache (production)
• deploy: github actions → ftp to cpanel on push to main

setup

npm install
npm start
# open http://localhost:3000

requires camera access. works on desktop and mobile browsers.

license

apache 2.0