121–130 of 421 posts
Earlier quoted context omitted.
I don't really buy this study. If humans can't get a better accuracy than 55%, I'm convinced this vector is leaking some obvious (maybe high-frequency) information that doesn't have anything to do with the face. E.g location of the person.
I hope you're right, otherwise we're in for some kind of AI phrenology nightmare in the not too distant future.
Earlier quoted context omitted.
"VGGFace224 was used to convert facial images into face descriptors, or 2,048-value-long vectors subsuming their core features." So they had more than age, race, and gender, but it doesn't really say how things were weighted.
I don't really buy this study. If humans can't get a better accuracy than 55%, I'm convinced this vector is leaking some obvious (maybe high-frequency) information that doesn't have anything to do with the face. E.g location of the person.
The only relevant 55% that I can find is:
> Allport and Kramer (1946) randomly presented 20 yearbook photographs of Jews and Non-Jews to 223 undergraduate students for 15 s each and asked them to categorize the person in each photograph as Jewish or non-Jewish, or to pass on the trial by indicating a lack of knowledge. The reported median identification for the sample was slightly above chance (55.5%; Allport & Kramer, 1946). Moreover, they found that highly prejudiced people were more accurate at distinguishing Jews from non-Jews
So it's not the same set of photos and not the same question.
> These self-selected, naturalistic images combine many potential cues to political orientation, ranging from facial expression and self-presentation to facial morphology. I was going to ask how they controlled for the presence of compound bows & deer, trucks, and tank tops in the photos, but this implies they did not. Another one would be lighting palette, since that's going to be biased to regions as well. There is…
Earlier quoted context omitted.
"VGGFace224 was used to convert facial images into face descriptors, or 2,048-value-long vectors subsuming their core features." So they had more than age, race, and gender, but it doesn't really say how things were weighted.
I don't really buy this study. If humans can't get a better accuracy than 55%, I'm convinced this vector is leaking some obvious (maybe high-frequency) information that doesn't have anything to do with the face. E.g location of the person.
1. The data:
"We used a sample of 1,085,795 participants from three countries (the U.S., the UK, and Canada; see Table 1) and their self-reported political orientation, age, and gender. Their facial images (one per person) were obtained from their profiles on Facebook or a popular dating website... Facial images were processed using Face++37 to detect faces. Images were cropped around the face-box provided by Face++ (red frame on Fig. 1) and resized to 224 × 224 pixels."
2. The benchmarks:
"For example, when asked to distinguish between two faces—one conservative and one liberal—people are correct about 55% of the time."
3. The controls:
"What would an algorithm’s accuracy be when distinguishing between faces of people of the same age, gender, and ethnicity? To answer this question, classification accuracies were recomputed using only face pairs of the same age, gender, and ethnicity."
A. A complaint:
Geography and income are two powerful conditioners. These can leak in so many ways: uncropped background (geography), image color and quality (income), eyeglass shape (geography and income). This study really needs more controls. Geography and income would be a nice start.
Earlier quoted context omitted.
"VGGFace224 was used to convert facial images into face descriptors, or 2,048-value-long vectors subsuming their core features." So they had more than age, race, and gender, but it doesn't really say how things were weighted.
I don't really buy this study. If humans can't get a better accuracy than 55%, I'm convinced this vector is leaking some obvious (maybe high-frequency) information that doesn't have anything to do with the face. E.g location of the person.
> These self-selected, naturalistic images combine many potential cues to political orientation, ranging from facial expression and self-presentation to facial morphology. I was going to ask how they controlled for the presence of compound bows & deer, trucks, and tank tops in the photos, but this implies they did not. Another one would be lighting palette, since that's going to be biased to regions as well. There is…
No need to read into implications. From TFA:
"The procedure is presented in Fig. 1: To minimize the role of the background and non-facial features, images were tightly cropped around the face and resized to 224 × 224 pixels."
While tightly cropping the face isn't perfect, it does address three of the four things you were specifically wondering about.
Earlier quoted context omitted.
"VGGFace224 was used to convert facial images into face descriptors, or 2,048-value-long vectors subsuming their core features." So they had more than age, race, and gender, but it doesn't really say how things were weighted.
I don't really buy this study. If humans can't get a better accuracy than 55%, I'm convinced this vector is leaking some obvious (maybe high-frequency) information that doesn't have anything to do with the face. E.g location of the person.
You think teachers are underpaid? Oh obviously you must be a pro-abortion, $15 minimum wage supporting, transgender-rights activist.
What's that you say, Christian bakers should be allowed to refuse to bake a cake with a pro-gay message on it? Oh, you must be a gun-toting, pro-life, anti-immigratnt Trump fanatic.
This kind of sorting people into simple binary categories, and giving them a "shopping bag" full of opinions they're supposed to hold helps nobody.
I'm not sure how this was relevant in anyway to your comment, but I just kinda jumped on my soapbox there.
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