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PRANJUL RATHOUR
PRANJUL RATHOUR

Posted on Originally published at pranjulrathour.scult.in

Liveness detection basics: stopping a photo from unlocking a face check

#liveness #antispoofing #facerecognition #security

A recognition model will happily match a printed photograph of you to your enrolled embedding. It was trained to recognise faces, not to notice paper. Liveness detection is the layer that asks "is there a live person here?" and FaceVision runs it in the browser alongside recognition.

Two families of liveness

  • Passive — a model looks at a single frame or a short clip for signs of a spoof: paper texture, screen moiré, unnatural depth cues. Frictionless for the user; only as good as its training data.
  • Active — the user is asked to do something: blink, turn their head, follow a dot. Cheap to implement with landmarks, hard to fool with a static photo, defeatable by a video replay unless the challenge is random.

Combine them

A passive check on every frame plus a randomised active challenge at enrolment and for high-value actions covers the common attacks: printed photos, phone screens, and pre-recorded videos. Neither alone does. Keep the challenge short — two actions — or users abandon the flow.

Doing it in the browser

Landmark tracking from the detector already gives you eye openness and head pose, so blink and turn challenges cost nothing extra. A small passive anti-spoofing model in ONNX runs in a worker alongside recognition. Frames stay on the device, which matters: liveness is where you would otherwise be streaming a user's face to a server continuously.

Measuring it honestly

  1. Collect real spoof attempts — photos on paper, on a phone, a video on a laptop — from several people and devices.
  2. Report the spoof acceptance rate and the live rejection rate separately. A system that rejects 20% of real users is not "secure", it is unusable.
  3. Re-test whenever you change cameras, lighting assumptions or the model.

Liveness is the part of a face project that shows a reviewer you thought about how it would be attacked. Recognition gets the demo; liveness gets the job.

About Pranjul Rathour

Pranjul Rathour speaking from the podium at VSICS, Kanpur
At the VSICS podium, Kanpur

Pranjul Rathour holding a trophy and a certificate of merit after a win
Trophy and certificate after a win

Pranjul Rathour presenting evaluation criteria — feasibility, innovation, practicality, problem solving — on a projector screen
Walking a room through evaluation criteria

Pranjul Rathour presenting BrandHive on a projector screen
Presenting BrandHive

Pranjul Rathour in a shirt and tie holding a microphone in front of a career-opportunities slide
A career session for students

Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-06.

Reach out if you want to talk GenAI, book a campus session, or invite him to judge:

Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: pranjulrathour41@gmail.com · Invite me to your campus
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