Computerized facial recognition has been progressing for something like 50 years, with each paper building on previous work. I suspect a parallel world where the dominant social group hard dark skin would explore different technical avenues, focus on ways of recognizing faces that lean less on tone, etc. Obviously it's hard to say with what we know now.
Humans have difficulty recognizing cross-ethnic faces, however I don't think dark skinned people are worse at recognizing same-ethnic faces than light skinned people. To me that suggests our social history is more to blame than some fundimental quality of skin tone.
This can extend beyond the film or image sensor to biasing lighting equipment, defaults, standards, and thresholds.
Looking through the literature you'll see things like, "the design decisions in the NTSC’s test images effectively biased the format toward rendering white people as more lifelike than other races"
https://sterneworks.org/Mulvin-Sterne-Scenes.pdf
I'm mostly familiar with the history in the US. I'm earnestly curious about how other countries accommodated these types of things as they adopted the technologies.
1. https://petapixel.com/2015/09/19/heres-a-look-at-how-color-f...
https://www.npr.org/2014/11/13/363517842/for-decades-kodak-s...
but, yes, dark skin reflects less light than light skin. by definition.
The camera settings and background lighting make a big difference.
Some notes:
- the eye can handle the dynamic range, but there's no correct exposure for that photographic combination, so they play with lighting, exposure bracketing and dodging when printing
- it's recommended that brides wear off-white (like cream) or other colors of fabric if they're concerned about the photos
Mind you, the hobbyist uncle may produce great photos an may be a wizard with lightroom and photo shop. But a lot of hobbyists try to compensate with gear for skill, or more charitable may not be even aware of related skills an possibilities. So in the end the snapshots your friend Andy took with his 2 year old iPhone might look better (have better automatic processing) than those photos Herbert took with his expensive prosumer or even pro kit.
Seeing how many great photos come out of the event in sometimes very strange lighting conditions (as you might expect from an event that includes everything from brightly lit outdoors events to indoor conference spaces and theaters to rock show-lit concerts, and everything in between), I'd imagine that little metering trick is doing wonders.
https://www.quora.com/Why-are-all-bodybuilders-tan-Do-they-u...
Moreover capturing Black/Dark skin and features requires more accurate light metering & lighting because dark skin absorb more light. There's a lot variance in cheekbones, nose and lips.
Humans' features in general, are more complicated then you realize.
I'm curious how many white people there are on earth vs how many black people there are, and other races. A couple google searches didn't give me any easy finds
The US is the only country I am aware of who still uses this term, everywhere else was using some thinking like ethnicity to indicate different culture, origin...
https://www.tandfonline.com/doi/abs/10.1080/0031322050034783...
This meme dates back to a loose claim made by R. Lewontin back in the 70s. In fact, you can very precisely and reliably recreate the "intuitive" human racial categorization using unsupervised algorithms, like doing multi-dimensional clustering over fixation indices. (It does not work using single-dimensional clustering, which is what Lewontin was talking about.)
Modern biologists usually talk in terms of clines rather than races, but this is just using the first derivative instead of the zeroth - you'll get the same result either way.
> Africa actually has more human genetic variation than anywhere else.
SNP diversity has ~nothing to do with phenotypic variance.
Not really - almost everyone can agree on "middle eastern/north african", "east asian", "south asian", "black african", "white", etc. If you force people to pick a single-digit number of major categories, they're probably going to come up with the same categories that k-means in fixation space would.
That almost everyone can agree on these categories is also contrary to reality. For example, many of who you describe as East Asians consider themselves racially distinct both within their societies and from their nearby neighbors. Also, what major categories do mixed race people fall inside?
> many of who you describe as East Asians consider themselves racially distinct
That's why I specifically mentioned the number of racial categories involved. Obviously as the number increases you can have different clustering results.
> Also, what major categories do mixed race people fall inside?
Obviously not into any of them, if we're talking about a simple mechanical classifier with high separation.
It may not be “your job” to provide such evidence, but you’ve made a series of specific claims about things like the rate of phenotypic variance among different racial groups. If you don’t want to defend them, that’s your prerogative, but you also can’t expect them to be received as authoritative or remain free of challenge.
Even the man who coined "Caucasian" as a racial category recognized that there was more physical variance among African populations and individuals than compared with Europeans.
> some who have naturally blonde hair and some who have blue eyes
I know they exist, but we are talking about statistical properties of entire populations, and these people are very rare.
> Humans' features in general, are more complicated then you realize.
It's not about what I realize - it's about what can be mechanically detected.
You missed this part: "Moreover capturing Black/Dark skin and features require more accurate light metering & lighting because dark skin absorbs more light."
I have to see the images used to train the ML model, to be certain, but based on my experience working in photography and programming, I believe it is more likely than not that they used essentially poor quality images for the training.
Moreover, after the model has been trained, to use the system effectively the facial recognition camera has to be set up to capture both light and dark skin, in the case of dark skin, it typically means not relying on available light alone indoors, an additional camera light must be provided.
The reality is if you want a facial recognition system that accurately detects dark skin it will cost a bit more to do it right.
But if it were so, then it sort of invalidates your argument because the bias complaints are that the facial recognition algorithms often misidentifies other races (not generally as white but as perhaps not human)
Specifically the scandal some years ago when black people where being identified as gorillas, it seems obvious to me that if non-white people have less facial variance it should be easier to identify black people as black instead of more difficult.
I thought it was a basic understanding that unfamiliar people, events, places, etc _do_ look alike, because before you get sufficient experience and exposure, you don't have enough skill to know what features are important to focus on and which ones carry no information.
It's also worth considering what saying that implies to the people you're saying it to. Especially when said dismissively. I'm terrible at names, faces, voices, pretty much any way of telling people apart. But that's my problem, and I need to be careful not to push the burden of that problem onto the people around me.
The first one CAN also be troubling if you have had ample exposure to become familiar, and are indirectly admitting that you did not believe it to be an important skill to learn.
I'd say it's an ignorant thing to say in the way you say it. Like you said in this post, you just don't know the features to look out for when you're not familiar with them
You might be right in terms of hair or eye color (not too many gingers outside Europe), but Africa has a vast amount of genetic diversity, so I'd naively guess that variance along other feature dimensions would be correspondingly higher too.
This is one bias I think benefits folks with dark skin. I'd love to be in the group that doesn't get recognised by this terrifying tech.
And the truth is, the danger zone is when the algo is somewhat ok, but a few percents worse on the discriminated group. If it were obviously stupidly bad (like for example any two black people are a positive match according to it) then people will disregard it. But if its just slightly bad thats harder to notice and suddenly your loan/car rental is rejected because the security system says you look too much like a specific known fraudster. And of course they wont even tell you that thats the reason, because security.
The indications in question were extremely rare for black people on the other hand. Another use case was the detection of Vitiligo, which is significantly more easy on black people. There was a great demand for therapies because patients are stigmatized for different reasons, especially in Africa because the symptoms can look similar to some deadly diseases.
There is a lot of discrimination in imaging, but it doesn't have to do with race and more with the creation of binary images.
As for facial recognition. I see some use cases for convenience, but it would be far more effective to disallow widespread application because of the numerous unsolved problems. So I see the move from IBM positively, even if they continue their work. In the end I believe face reg to be possible without bias, but I don't have these propped up security needs.
If your a member of Congress who is also a person of color, it will do that at twice the rate of your white colleagues. [0]
[0]: https://www.aclu.org/blog/privacy-technology/surveillance-te...