The technology, which marries Meta’s smart Ray Ban glasses with the facial recognition service Pimeyes and some other tools, lets someone automatically go from face, to name, to phone number, and home address.
For low contrast greyscale sequrity cameras? Sure.
For any modern even SD color camera in a decently lit scenario? Bullshit. It is just that most of this tech is usually trained/debugged on the developers and their friends and families and… yeah.
I always love to tell the story of, maybe a decade and a half ago, evaluating various facial recognition software. White people never had any problems. Even the various AAPI folk in the group would be hit or miss (except for one project out of Taiwan that was ridiculously accurate). And we weren’t able to find a single package that consistently identified even the same black person.
And even professional shills like MKBHD will talk around this problem during his review ads (the apple vision video being particularly funny).
You’re not wrong. Research into models trained on racially balanced datasets has shown better recognition performance among with reduced biases. This was in limited and GAN generated faces so it still needs to be recreated with real-world data but it shows promise that balancing training data should reduce bias.
Yeah but this is (basically) reddit and clearly it isn’t racism and is just a problem of multi megapixel cameras not being sufficient to properly handle the needs of phrenology.
There is definitely some truth to needing to tweak how feature points (?) are computed and the like. But yeah, training data goes a long way and this is why there was a really big push to get better training data sets out there… until we all realized those would predominantly be used by corporations and that people don’t really want to be the next Lenna because they let some kid take a picture of them for extra credit during an undergrad course.
Facial recognition works better on white people because, mathematically, they provide more information in real world camera use cases.
Darker skin reflects less light and dark contrast is much more difficult for cameras to capture unless you have significantly higher end equipment.
For low contrast greyscale sequrity cameras? Sure.
For any modern even SD color camera in a decently lit scenario? Bullshit. It is just that most of this tech is usually trained/debugged on the developers and their friends and families and… yeah.
I always love to tell the story of, maybe a decade and a half ago, evaluating various facial recognition software. White people never had any problems. Even the various AAPI folk in the group would be hit or miss (except for one project out of Taiwan that was ridiculously accurate). And we weren’t able to find a single package that consistently identified even the same black person.
And even professional shills like MKBHD will talk around this problem during his review ads (the apple vision video being particularly funny).
You’re not wrong. Research into models trained on racially balanced datasets has shown better recognition performance among with reduced biases. This was in limited and GAN generated faces so it still needs to be recreated with real-world data but it shows promise that balancing training data should reduce bias.
Yeah but this is (basically) reddit and clearly it isn’t racism and is just a problem of multi megapixel cameras not being sufficient to properly handle the needs of phrenology.
There is definitely some truth to needing to tweak how feature points (?) are computed and the like. But yeah, training data goes a long way and this is why there was a really big push to get better training data sets out there… until we all realized those would predominantly be used by corporations and that people don’t really want to be the next Lenna because they let some kid take a picture of them for extra credit during an undergrad course.
You okay?