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.