Facial recognition is an algorithm applied to ordinary picture and video data, it does not require special or even particularly high-quality cameras. In fact, if the footage is too high-quality it actually has to be downscaled before being analyzed; something like a picture of your face taken with a 720p laptop webcam when sitting at the laptop, or a video of your face from a 1080p Ring doorbell camera as you go to ring the bell, would likely be already big enough to require downscaling first.

Yep, typically an algorithm with heavily baked in bias. Works great for specific populations, not so much when it needs to recognize every possible facial structure on the planet - something that's a problem in very few places, except that NY is basically #1 on the list of places where you algorithm has to work for every possible face on the planet, and would have to be tamper proof, which by definition it can't be unless we augment it with IR and lidar to make sure there's a human standing in front of it. And then that would have to still work when it's both 100F and -40F. You know what lets people into your building even more efficiently? A key fob.

Addressing this misconception is the whole point of my original comment, actually. Typically the algorithms used work great on all populations and do not have heavily baked in bias (in the technical sense; they do still have bias in the social justice sense of “automating the detection of criminals, who are disproportionally minorities because society’s bias has disproportionately criminalized minorities”). The field has generally moved away from specific algorithms that analyze facial structure and towards much more effective machine-learning-augmented methods (they got Bitter-Lessoned just like other areas of technology did). Facial recognition has literally already been used to great effect in New York for years already, that’s why it is New York that is leading the way in regulating face rec, because they interact with it the most (apart from maybe Australians at Australian airports).

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.

>Typically the algorithms used work great

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.

Respectfully, I do have a much better idea than most people about what is being deployed and used, as I built some of them.

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'm with you all the way on your points so far. I think at this point it will be important to refer to a specific product, one that's available to landlords.

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.

Go ahead and buy an el-cheapo webcam and test the facial recognition software that is built-in. I challenge you to be able to reliably use that to recognize a face from a block of cheese.

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.

If some kind of facial recognition algorithm/model can use worse inputs to "do better than humans" in some task (i.e. try to recognize between twins using a downscaled image from a 720p laptop camera) more often than not focusing in other things different from the face itself or there is some kind of target leakage in the datasets. More often than not those are big reasons why is so difficult to target SOTA paper performance in the real world, be it in face recognition or otherwise.

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).

I won’t say it’s impossible, but your suggestions do seem unlikely to me. We’re talking about unlabeled data, cropped to just the face, and the state of the art being found in commercial models in the real world rather than in papers competing to pass standard tests in the literature.

(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”.)

This reasoning (“it needs to be worse to be better”) is indistinguishable from homeopathy.

I meant that it is limited by the size of the input layer. A 2000x2000 pixel image of a face has to be downscaled, because otherwise you’d need 4 million nodes on the input layer and that’s impractical.

Sounds like NYC needs to add more housing supply to take the power away from corporate landlords.

For the landlord situation, what's the additional harm here if the facial recognition system is inaccurate for people of a certain skin color?

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)

Police/security could be called on a resident every time they walk into the hallway of their building, and landlords would tell them "we've put in a support ticket with our facial recognition vendor, we'll let you know their response in 4-6 weeks".

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.