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"Their method is straightforward. They take ID photos of 1856 Chinese men between the ages of 18 and 55 with no facial hair. Half of these men were criminals.
by jbpetersen 10y ago
"Their method is straightforward. They take ID photos of 1856 Chinese men between the ages of 18 and 55 with no facial hair. Half of these men were criminals.
They then used 90 percent of these images to train a convolutional neural network to recognize the difference and then tested the neural net on the remaining 10 percent of the images.
The results are unsettling. Xiaolin and Xi found that the neural network could correctly identify criminals and noncriminals with an accuracy of 89.5 percent."
- deleted 10y ago[deleted]
- rdlecler1 10y agoI'd like to see what human recognition would be as a null hypothesis. You could also block out certain features to see what's driving the decision making here.
- jbpetersen 10y agoThe article details the specific facial features that were focused on by the trained model. So that's at least part of what you're looking for.
- ganeshkrishnan 10y ago89.5 accuracy is real bad. It looks pretty convincing but the probability of a person being a criminal turns out to be pretty low. Cancer detecting machines have probability close to 99% but even if it says you have cancer the probability is not that high (close to 10% I think) because of false positives. Likewise if this network predicts a user to be criminal when he is not then the probability of success goes down.