EyesOff: How I built a screen contact detection model

It’s an application which detects people looking at your screen. The aim is to keep you safe from shoulder surfing, utilising your webcam to give you the power to prevent snoopers.

When is this ever a problem that cannot be solved by positioning yourself with a wall behind you or going somewhere private? This feels like overkill for the stated use-case. I can imagine someone thinking they might need this to do private stuff in a public space (a coffee shop?), but they'd turn paranoid from everyone passing by just glancing around.

Also, is this a realistic threat model anywhere? People snooping by standing behind you tend to be colleagues or totally random passers-by; not people actually interested in gleaning private information. Anything more serious than logging into your Facebook account would imply simply having proper OpSec procedures (like: 'only do this in private').

All I can think of is employee monitoring where such tools will just end up making people insecure in their workplace (and less productive, because gazing out of a window or into nothingness actually helps when you are doing work which requires pondering; and less healthy, because looking away from your screen into the distance is recommended for anyone with working eyes).

So much text and not a single example, diagram, or demo.

I'm honestly skeptical this will work at all, the FOV of most webcams is so small that it can barely capture the shoulder of someone sitting beside me, let alone their eyes.

Then what you're basically looking for is callibration from the eye position / angle to the screen rectangle. You want to shoot a ray from each eye and see if they intersect with the laptop's screen.

This is challenging because most webcams are pretty low resolution, so each eyeball will probably be like ~20px. From these 20px, you need to estimate the eyeball->screen ray. And of course this varies with the screen size.

TLDR: Decent idea, but should've done some napkin math and or quick bounds checking first. Maybe a $5 privacy protector is better.

Here's an idea:

Maybe start by seeing if you can train a primary user gaze tracker first, how well you can get it with modeling and then calibration. Then once you've solved that problem, you can use that as your upper bound of expected performance, and transform the problem to detecting the gaze of people nearby instead of the primary user.