Nami

Nami - Face Recognition Photo Gallery (early preview)

Please read carefully the description

Nami is a privacy-first face recognition photo gallery for Sailfish OS. It scans your photo gallery, detects faces, and lets you tag and group photos by person, entirely on your device.

⚠️ This is a preliminary, experimental release (v0.7.0). Expect rough edges.

Source code: https://github.com/nicosouv/harbour-nami

About this project

This app is written by a human developer with an LLMs (Claude and Mistral Vibe). It is also an experiment: testing how to package a Sailfish OS app together with its ML models fully bundled, so it runs 100% offline out of the box.

ML models

The models are NOT downloaded at runtime. They are fetched during the CI build (SHA-256 checksum-verified) and shipped inside the RPM. Both come from the OpenCV Model Zoo:

- YuNet (face detection, MIT license)
Source: https://github.com/opencv/opencv_zoo/tree/main/models/face_detection_yunet
Direct file: https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx
- SFace (face recognition, Apache-2.0 license)
Source: https://github.com/opencv/opencv_zoo/tree/main/models/face_recognition_sface
Direct file: https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx

Security, privacy & anonymity

- All face detection and recognition runs locally on your device (C++ / OpenCV DNN). No cloud, no external API.
- The app makes no network requests at runtime: photos, face embeddings and names never leave your phone.
- Model files are checksum-verified at build time so you get exactly the published OpenCV Zoo models.

Data storage & deletion

- Face data: detected faces, biometric embeddings, the names you assign, and manual corrections is kept in a local SQLite database on the device only.
- The database is stored with owner-only file permissions (0600), so other user accounts or processes on the device cannot read it. Note: it is not encrypted, the security model relies on the device's own storage and lock-screen protection.
- The only app settings persisted are non-personal: the folder to scan, the recognition strictness threshold, and an internal embedding-format version. No names or biometric data are stored as settings.
- One-tap "Clear all data" (in Settings, with a confirmation) wipes every photo record, detected face, biometric embedding, person name and manual correction, then runs a database VACUUM to purge the deleted data from freed pages. It also deletes the cached face-crop images from disk.

Feedback

This is an early version, if the app doesn't work for you or you dislike something about it, I'm genuinely open to constructive criticism.

Please report issues or ideas on GitHub: https://github.com/nicosouv/harbour-nami/issues