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Comment by xmichael999 | Hacker News Reader

I don't know too much about facial recognition technology (I work more in the ALPR space), but I know enough to tell you that a single photo is not enough to train the software to do anything useful. Either they were sold a dream by a company making bogus claims, or they just don't give a shit about false postives in the slightest and they use it as justification to randomly stop and search people. There was an article about the UK doing this https://www.engadget.com/2019/07/04/uk-met-facial-recognitio... I can't find the hacker news link, but its worth a read.

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A single picture is surprisingly effective in recognizing that person in another photo. I've been playing around with a python wrapper on dlib's facial recognition [0] and it's astonishing how it can identify people from just one photo. It's not like other machine learning methods that include training a classifier or even fine tuning a classifier. It works by encoding the persons face to a 128 dimension vector and then using that for comparison.

The accuracy from most methods is between 99.2% - 99.8%, but the problem is that the training samples are too easy and controlled. It's sensitive to lighting. Google's most recent paper [1] on Facenet found 99.63% on the easy Labeled Faces in the Wild (LFW) dataset, and an impressive 95.12% on the Youtube faces dataset, presumably a much more difficult dataset.

[0] https://github.com/ageitgey/face_recognition

[1] https://arxiv.org/abs/1503.03832

Accuracy depends on the dataset like you point out. It's a hard task to find a person among all driver license photos in the US.

Anecdotally, FindFace found my Russian friend's VK.com profile (amongst many millions of such profiles) as the number one hit after I took a picture of her in a bar and submitted it to their face recognition service.

I think your example is different to this scenario as your friend will have submitted many pics to VK so they have decent training set for her face.

Whereas the DMV only has 1 picture of a suspect so it will result in more false positives.

dlib's face recognition is unsuitable for large-scale (a million faces) or single-reference recognition tasks.

These libs assume the angle is the same, i.e. square shot of the face. Cameras mounted up high are completely useless with these types of libs. Unless the tech. exists to magically rerender a front face shot so you can image how it would look from 12 - 20 feed raised up, this doesn't work.

I wonder what happens if we take a square shot of a face, then use something like the deep fakes to generate different predicted views and then feed those in to the original system.

You get Omphaloskepsis.

Is this really a thing? Man, you learn something every day... I am not sure I needed to know about this though :/

That might be an interesting problem for a generative adversarial network. Train it based on some large corpus of faces and then refine it based on searching for the input vector that most closely matches the original image and then just change the pose in that input vector to generate a square shot of the face. In theory this would give you not only some generated face, but some reasonable space of facial features that the discriminator couldn't reject. I.e. beard / no beard if the chin was obscured.

Paid for by the same IARPA contract as dlib: https://talhassner.github.io/home/projects/frontalize/hassne...

> Either they were sold a dream by a company making bogus claims, or they just don't give a shit about false postives in the slightest and they use it as justification to randomly stop and search people.

You hit the nail on the head. Any system that hinders law enforcement’s assumptions and hunches will be touted as defective, anything that confirms those assumptions will be exalted.

This is why narcotics dogs are used. Studies show that dogs will signal upon trainer instruction, and that dogs aren’t accurate nor precise in practice. Such dogs exist to establish probable cause, not to determine whether or not a suspect possesses narcotics.

It probably happens unconsciously a lot too, with no specific ill intent ... officer just has a "bad feeling" about a person and the dog detects the tension. Dogs are very sensitive to shifts in human emotion.

>or they just don't give a shit about false postives in the slightest and they use it as justification to randomly stop and search people.

We already have this. Look up the false positive rate of drug sniffing dogs.

This. Easiest way to circumvent the 4th amendment. Get the dog to bark.

Yup. Basically, it's Clever Hans (https://en.wikipedia.org/wiki/Clever_Hans) weaponized against the populace. Doesn't even have to be conscious on the part of the handler.

I would say that false positives are a feature. Like you mentioned it gives authorities cause to atop/investigate you. If they are wrong they have deniability. If they are right then mission accomplished!

Ouch, a punch right in the fourth ammendment.

Yah, one only needs to use drug dogs as an example here. It is a known fact the dog can be triggered to claim a hit etc... and somehow in most states a dog doing "something" is sufficient justification to have your vehicle searched..

This might be the HN thread you remembered https://news.ycombinator.com/item?id=20364523

Don't you have to renew your driving license photo every few years?

If the old photos are still on file, having multiple photos in the same style of a person a few years apart is actually probably quite a good training set.

My current driver's license photo is over a decade old and doesn't expire until 2023. At least when I renewed my license they didn't want to update the photo of me when I was 16 and just renewed it for 8 years. In theory I'll probably need to update my photo then, but as it is right now I look nothing like the photo on my driver's license.

Oh you know its both

modern data augmentation techniques can be very effective especially when fine-tuning already pretrained model. But still very much work in progress

[deleted]

I read the summary, and I don't agree. The tests being done are used under special circumstances. No way a camera mounted 12 or 14 feet up at an angle is going to provide the same type of results as youtube videos which are at a flat angle. There are simply too many "angle" like issues that will result in the accuracy being quite poor.

I didn’t read the story carefully because of the paywall, but it depends on what the searches are doing and what the sources are.

Several states have had success in identifying people with multiple drivers licenses using facial recognition systems on their photosets. Of course they are using DMV photos that are taken in a consistent way.

FBI or ICE could very well have a feed of identification photos from foreign countries that are close enough to get good success.

You train the neural net to encode landmarks which are unique to individual faces. Such a net, when trained, can extract the unique facial features from a single image and embed them in a vector to be compared to features extracted from other images.

This is similar to how single images of faces can be animated with GANs now.

https://arxiv.org/abs/1905.08233v1

Is this the same type of neural net that's powering "deepfakes"?

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