The deep state is now trying to determine if someone is a criminal based solely on an image of their face. A Cornell University study describes how scientists are using computer generated algorithms to decide if someone should be labeled felonious, completely ignoring the fact that using a computer model which mimics facial features cannot possibly be a legal means with which to label people either criminal or non-criminal.
Nonetheless, scientists used facial images of 1856 real persons, “controlled for race, gender, age and facial expressions, nearly half of whom were convicted criminals, for discriminating between criminals and non-criminals. All four classifiers perform consistently well and produce evidence for the validity of automated face-induced inference on criminality, despite the historical controversy surrounding the topic.”
This means that computers and scientific studies funded by an elite class will now determine if someone is criminal in nature.
In the study titled “Automated Inference on Criminality using Face Images,” scientists Xi Zhang and Xiaolin Wu state,
“Above all, the most important discovery of this research is that criminal and non-criminal face images populate two quite distinctive manifolds. The variation among criminal faces is significantly greater than that of the non-criminal faces. The two manifolds consisting of criminal and non-criminal faces appear to be concentric, with the non-criminal manifold lying in the kernel with a smaller span, exhibiting a law of normality for faces of non-criminals. In other words, the faces of general law-biding public have a greater degree of resemblance compared with the faces of criminals, or criminals have a higher degree of dissimilarity in facial appearance than normal people.”
The absurdity of this claim aside, it is not a new idea. Cesare Lombroso, an Italian criminologist, believed that criminals were “throwbacks” more closely related to apes than law-abiding citizens. He was convinced he could identify them by ape-like features such as a sloping forehead, unusually sized ears and various asymmetries of the face and long arms. Indeed, he measured many subjects in an effort to prove his view although he did not analyze his data statistically.
Lombroso’s views were discredited by an English criminologist named Charles Goring, who statistically analyzed the data relating to physical abnormalities in criminals versus noncriminals. He concluded that there was no statistical difference, but this didn’t sway the elite from trying to find a plausible scientific argument for their AI platform.
Using an AI, machine-based algorithm, Xiaolin and Xi claim that their neural network can correctly identify criminals and noncriminals with an accuracy of 89.5 percent.
What differences do they find in the criminal face?
The curvature of upper lip is an average 23 percent larger for criminals than for noncriminals according to Xiaolin and Xi. The distance between two inner corners of the eyes, is 6 percent shorter; and the angle between two lines drawn from the tip of the nose to the corners of the mouth is 20 percent smaller.
Now just imagine if this AI technology is used to analyze your passport or driver’s license, and you are pulled aside before you’ve ever committed a crime by law-enforcement, dubbed a law-breaker simply because your eyes are too close together or your upper lip doesn’t curve a certain way. Ignore all racial, emotional, and cultural difference that might cause said changes to a human face, as well.
The movie Minority Report hinted at this type of Deep State technology, but now it’s being fleshed out in scientific journals.
Add to this the AI being used in security cameras, and drones to detect “problematic” or “criminal” behavior, along with police departments who are already using artificial intelligence to “stop crimes before they happen” and we’re all in for much more than a Brave New World.
Read More Articles by Nathaniel Mauka.
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This article (Deep State AI Algorithms will Scan Faces to Determine Criminality) was originally created and published by Waking Times and is published here under a Creative Commons license with attribution to Nathaniel Mauka. It may be re-posted freely with proper attribution and author bio.