Point your camera at your face. A model running entirely inside your browser —
no upload, no server, no account — will infer your emotional state, estimate your age
and gender, map 68 biometric landmarks, and derive an identity hash from your facial geometry.
This experiment transmits nothing. Real-world deployments do not make that promise.
Passive face inference is already standard infrastructure.
Airport kiosks, supermarket self-checkouts, digital billboards, ATMs, and smart doorbells
routinely estimate age, gender, attention, and emotional state — without disclosure.
In the EU this is technically regulated. In practice it runs everywhere, in silence,
at 30 frames per second.
loading models — first run takes ~10 seconds...
initializing...
dominant emotion—
confidence—
estimated age—
estimated gender—
keypoints mapped—
inference rate—
face geometry hash — stable across expressions— (no face detected)
emotion probability distribution
neutral
0%
happy
0%
sad
0%
angry
0%
fearful
0%
disgusted
0%
surprised
0%
▶ what is the model actually measuring?
Three neural networks run in sequence on every frame:
Face detector — locates bounding boxes at multiple scales. Sub-30ms on modern hardware.
Landmark network — maps 68 anatomical keypoints: eye corners, nostril base, lip edges, jawline. Sub-millimetre resolution relative to face size.
Expression classifier — reads muscle activation patterns from landmark geometry. 7 classes: neutral, happy, sad, angry, fearful, disgusted, surprised.
Age / gender estimator — regression over tens of thousands of labeled images. Age ±5 years; gender binary probability.
The geometry hash is derived from expression-stable ratios: inter-ocular distance, face width/height, nose width, mouth width. These change slowly, if at all, over years. They are the basis of face recognition systems at scale.
Everything runs client-side. Nothing leaves your device. That is the exception, not the rule.