Nose ID: I Taught an AI to Recognize Dogs by Their Nose-Print

A developer built Nose ID, a biometric system that identifies dogs by their unique nose-prints using a pretrained MobileNetV2 as a feature extractor and cosine similarity for matching. The system also uses Google Gemini to generate dog bios and ElevenLabs for voice confirmation. The developer noted that both Gemini and ElevenLabs changed parts of their APIs during the same week the project was built.

Nose ID — a biometric dog identification system. Instead of a collar tag, chip scanner, or manual lookup, it identifies a dog the way security systems identify people: from a unique physical trait. In this case, the dog's nose-print — the ridge and texture pattern on a dog's nose, which is as individual to each dog as a fingerprint is to a human. Scan a dog's nose → the system tells you who it is with a spoken voice response or offers to enroll it if it's new. 1. Embedding extraction the core ML piece Rather than training a nose-print classifier from scratch, I used a pretrained MobileNetV2 ImageNet weights as a feature extractor, stripping its classification head and using the penultimate layer's pooled output as a 1280-dimensional embedding vector. This is a standard transfer-learning technique — mid-level CNN filters trained on general images still respond well to texture, edges, and ridge patterns, which is exactly what distinguishes one nose-print from another. 2. Matching Each enrolled dog's embedding is L2-normalized and stored. A new scan's embedding is compared against all stored ones using cosine similarity . A similarity above a tuned threshold 0.90 counts as a match. 3. AI-generated personality On enrollment, Google Gemini generates a short, fun bio for each dog from its name and breed. 4. Voice confirmation On identification, ElevenLabs turns the result into a spoken response — "Welcome back, Bella. Access granted." or "I don't recognize this nose. Would you like to enroll?" Both Gemini and ElevenLabs changed parts of their APIs the same week I built this: generateContent endpoint to their new /v1/voices to find one my account could actually use.This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. Currently, two official plugins are available: The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see this documentation https://react.dev/learn/react-compiler/installation . If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the TS template https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts for information on how to integrate TypeScript and typescript-eslint https://typescript-eslint.io in your project.