About the only thing I don’t take issue with in your post is LIDAR/IR to verify it’s a real human and not a piece of paper with a face printed on it. If you’re using face rec to automate entry, yeah, you need to also validate it’s a real human face.

I hope you let the word "typically" do some stretching before you used it, because it's doing a lot of heavy lifting there. You have no idea what algo was used by each system. Assuming some landlord is going to install the top of the line equipment using the most advanced software with these algos that "work great on all populations" is just so generous that I want to see take advantage of that generosity.

Facial recognition is at best a parlor game. Yes, it can be super helpful for personal use in sorting through your own private library. If facial rec messes up in that situation, no harm is committed. When someone is denied access to their building, harm is committed. When someone is mis-identified and arrest warrants issued on an innocent person, harm is committed. To even begin to think that facial recognition is bullet proof in all instances is just folly.

I am not saying it is bulletproof. I am saying it will reliably pull 10 photos of the same person out of 100,000,000 total photos, for any given person, based off a single new photo it has never seen before. That’s the kind of scale and accuracy we’re talking about here, it’s not parlor games. And that 100M was small fries compared to bigger players - who I am sure were into the billions, with even better precision than we had.

I still don’t understand the multiple objections I see relating to “top of the line equipment” or advanced expensive software. The mental model you should have is something like Copilot or GPT3.5. The landlord is just buying access to a nice UI that makes API calls.

I think in the minds of commenters there is an idealised version of "face recognition", so in a way, everyone is arguing past each other.

Sure, the high quality algorithms are out there. But no one actually sells the hardware with that stuff built into it, at least not at a price that any sleazy landlord is going to be paying.

ML algorithms for FR are not magical and I don't expect them to be always better in all conditions than a human with two eyes (i.e. stereo vision).

(From my direct experience they arguably are pretty close to magical; I think there is just a lot more information in pictures of faces than we thought there was. It’s quite plausible that, since native human machinery was evolved in groups of maximum ~150 distinct faces and was optimized for things like detecting minor emotional cues, our brains are simply throwing a lot of that information out. But that’s not really the point I want to make here; instead, I want to say stuff like “commercial models aren’t trailing research papers; research papers are trailing commercial models”.)

From the article, the concern here is that the landlords are "trying to find ways to expedite ways of flushing people out of the building". Presumably the this tech could make it easy to tenants violating their lease. For example, detecting non-residents using keys to enter the building as a sign of illegal subletting.

But if the face rec system incorrectly identifies some lease violation, the landlord still needs to go through additional human verification and present proof to a judge to start the eviction process. It seems like inaccurate facial recognition would be good for the tenants. If the results are inaccurate enough it eventually defeats the whole purpose of the system.

(this is very different from situations like Madison Square Garden's use of face rec, where a someone might be denied entry unilaterally because of misidentification and have no recourse)

Or residents could be locked out of the building with no way to get in for weeks at a time.

Just facial recognition wouldn't have a point - landlords are going to use this data for something, and that something is likely to interfere with residents quiet enjoyment of their residence.

And I highly doubt that a "penny-pinching landlord" will install anything close to Sota.

Doesn't mean the car behind you at the red light will brake. Just like the fact that a model can tell twins apart doesn't mean the model the cops used can tell you apart from the actual criminal. Or that a Landlord will have tested the model at all or required it to meet some standard of twin detection.

Because your (several) comments defending this technology boil down to ‘trust me bro, I’ve seen it in the lab’.

Basically you should just imagine there was a media cycle about the dangers of unregulated trains that go 50mph, do not have any brakes, and need to be regulated because they’re running pedestrians over. And you should read my comments like I’m a pedantic trainwatcher saying “actually these trains go 400mph, can stop on a dime, and need to be regulated because they’re running pedestrians over. Source: I used to be a train driver”.

And if Nest is not deploying state of the art facial recognition then what are the odds that some random landlord is? I'm going to say close to none.

It was one of the first things I noticed when I got the doorbell. The difference was stark and obvious.

Actually, if Nest is representative of what most consumers experience when interacting with facial recognition, then I understand why there’s this common perception that facial recognition is buggy and unreliable. I’m drawing on my experience working on commercial models.

I heard this a lot twenty years ago, and even a little ten years ago.

Now, I agree that if the argument is "surveillance is inaccurate" then some landlords and landladies might come back and say, "ours is more accurate than trained crowds of professional forensic experts" and it's possible the activists would not want that and just don't want any surveillance, or maybe they are looking for accuracy and the elimination of misattribution.

I really just think that if activists see two stats like “provably 99.999…% accurate” and “provably impacts minorities more” at the same time, most of them are still going to care about the impact than the accuracy - and I don’t think that caring about the impact is in any way intellectually dishonest, it’s quite moral really.

While I'm sure some algorithm is provably unbiased, it's a little naive to imagine landlords (you know about the landlord special? Load bearing paint and the likes?) caring about what they are running.

You should make your point in reply if you disagree with OP, because slandering OP is not changing my pov to yours here.

That's SOA and in controlled circumstances you're talking about.

Not real-life FR using crappy camera, with bad lighting, against shitty lookup databases (with built-in biases).

Which is what's at issue, here.

On a clear pictures, maybe. On a regular blurry surveillance footage from a gas station camera on a day with bad weather and poor lighting, absolutely not.

And then I ask myself, is that really a bad thing? I am far more likely to be the victim of a crime than the perpetrator of one or the victim of inaccurate accusation. The places I’ve lived with pervasive surveillance were also some of the safest, and those who argue against this “oppressive” surveillance rarely are forced to balance that with the actual oppression of crime, or the second-order consequence of the lack of security cameras (that low level crimes will probably go unpunished).

I agree. We need to be fighting the panopticon. The more accurate it is, the more objectionable it is.

That may be true of state of the art models that have been demonstrated under some conditions; it continues not to be true, frequently, of models actually adopted in major products, even new ones, in the actual deployment conditions (e.g., the recently adopted CBP One app that US asylum seekers are required to use for appointment scheduling and other purposes, and which requires facial recognition, and disproportionately fails for dark skinned users, who are not uncommon in the population required to use the app.)