On 4 June 2026, a security researcher publishing under the name Buchodi released a technical analysis of Stella, the companion app for Meta’s Ray-Ban and Oakley smart glasses. Inspecting version 273.0.0.21 of the Android build, the researcher found what they described as a complete, dormant facial recognition pipeline: three on-device AI models, a biometric database schema, a vector similarity index dimensioned to those models, a write path for unrecognised faces, and a hardcoded notification channel labelled “nametags_recognition.” The research was published alongside reporting in WIRED, which confirmed that code had been added to the app across multiple updates since January 2026. The app, which is required to use the glasses’ key features, had been downloaded more than 50 million times before any of this was disclosed. What the researcher found in the app The three models identified in the Stella build are SCRFD, a face detection model developed by InsightFace; KPSAligner, which crops and aligns detected faces using facial keypoints; and SFace, which converts an aligned face into a 2048-number biometric fingerprint. The SFace variant in Stella appears to be scaled larger than the public reference implementation: 96 megabytes versus approximately 40 megabytes in the open-source version, with a 2048-dimension output. These models arrive on the device via Meta’s asset delivery system. Alongside the models, the researcher found a SQLite database stored under Meta’s cross-device sync framework, RLDrive, in a namespace called person_profiles . The database schema holds named person records, face records linked to each person, and a vector table dimensioned at exactly 2048 floats to match the SFace embedder, using cosine-distance search. Each face row links back to a person name. Recognition, when it runs, is a cosine-similarity query against the stored faceprints, followed by a join to retrieve the person’s name for the notification text. The researcher