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Google Pixel 'Project Toscana' reportedly upgrades face unlock hardware, compares to iPhone Face ID
Project Toscana aims to deliver secure, hardware-backed face unlock on Pixel phones and Chromebooks that works in varied lighting and matches iPhone Face ID speed.
- Behind the scenes, Project Toscana aims to upgrade face unlock on Google Pixel smartphones and Chromebooks, reportedly matching iPhone Face ID in various lighting conditions, Android Authority reports.
- After pulling back hardware, Google has slowly rebuilt camera-based face unlock in recent years, initially without secure-app support; the Pixel 4 shipped with a full IR face-unlock array that was later abandoned.
- Keeping a single hole-punch, the design reportedly includes infrared components to improve detection, as existing Pixel face-unlock performs well only in ideal light.
- If realized, Project Toscana could strengthen Pixel biometrics across devices by improving face-unlock reliability on Pixel phones and Chromebooks, reflecting Google's renewed emphasis on face unlock in the Android ecosystem.
- There are still many questions about how Google will implement the hardware, and earlier rumors linked an under-display IR camera and Tensor G6 to Pixel 11 face unlock claims.
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Google hopes that with Project Toscana they can count on their own facial recognition system for Android
Mario Romero.- While many phone manufacturers offer facial recognition, only the iPhone FaceID with 3D technology, it was not only the first, but it has managed to remain the most accurate and best result in the market after seven generations since its launch with the iPhone X. But obviously, its [...] The post Google develops its own FaceID technology for its next Pixel phones and promises to beat the iPhone first appeared on TransMedia.
On the Google side, we want to be able to compete with the Face ID of the iPhones. And that might well be the case with the next Pixel 11!
Coverage Details
Total News Sources16
Leaning Left2Leaning Right0Center1Last UpdatedBias Distribution67% Left
Bias Distribution
- 67% of the sources lean Left
67% Left
L 67%
C 33%
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