FaceVision does face detection, recognition and liveness checks without sending a single frame to a server, because every model runs in the browser through ONNX Runtime Web. That architecture is the reason its privacy story is simple. It is also the part students find hardest to get working, so here is the path that worked.
Export, then verify
Export the model to ONNX from its training framework and immediately run the same input through both the original and the exported graph. Compare outputs numerically. Every model in FaceVision was verified against its actual ONNX graph and, where possible, the reference implementation — not assumed from documentation. Half the "the model is broken in the browser" reports I have seen were pre-processing mismatches that a five-minute comparison would have caught.
Choose the execution provider deliberately
- WebGPU — fastest where available; check support and fall back gracefully.
- WASM with SIMD and threads — the reliable default; needs the right headers for multi-threading.
- WebGL — legacy; avoid for new work.
Pre- and post-processing is where bugs live
Models expect a specific channel order, normalisation and input size. Browser image data arrives as RGBA bytes in a different layout. Write the conversion once, test it against a known image, and keep the output tensors' shapes in a comment next to the code. Post-processing — decoding anchors for a detector, normalising an embedding before cosine similarity — deserves the same care.
Performance that feels live
- Warm up the session with one dummy inference on page load; the first run is always slow.
- Run inference in a Web Worker so the camera preview never stutters.
- Downscale frames before detection; run recognition only on detected crops.
- Skip frames when the queue is full rather than letting latency grow.
What the backend does when the model does not
In FaceVision the FastAPI and PostgreSQL backend stores only vector embeddings and matches them at enrolment and verification. Because no images are ever persisted, the privacy claim is easy to explain and audit. I wrote about the design reasoning in face recognition that never uploads a face.
Browser-side inference is not a gimmick. For anything involving faces, documents or health data, it is often the architecture that makes the product acceptable to the people using it.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
