With the help of a facial recognition system, federal agents could capture a man suspected of abuse. The tool detected him in the background of someone else's photo at the gym, in the mirror. So, the agents were able to get to that gym, ask about the man, and eventually capture him. This real-life story, and many others, encourage businesses to benefit from AI services and deploy facial recognition systems. The global facial recognition market size was evaluated at $3.8 billion in 2020 and is expected to reach $8.5 billion in 2025, growing at a CAGR of 17.2%.
A U.S. government study released this week found that 189 facial recognition algorithms from 99 developers "falsely identified African-American and Asian faces 10 to 100 times more often than Caucasian faces." This should be the last such study. We are long overdue for federal governments to regulate or outright ban facial recognition. This year, the NYPD ran a picture of actor Woody Harrelson through a facial recognition system because officers thought the suspect seen in drug store camera footage resembled the actor. This year China used facial recognition to track its Uighur Muslim population.
Microsoft's facial-recognition technology is getting smarter at recognizing people with darker skin tones. On Tuesday, the company touted the progress, though it comes amid growing worries that these technologies will enable surveillance against people of color. Microsoft's announcement didn't broach the concerns; the company merely addressed how its facial-recognition tech could misidentify both men and women with darker skin tones. Microsoft has recently reduced the system's error rates by up to 20 times. In February, research from MIT and Stanford University highlighted how facial-recognition technologies can be built with bias.
The researchers have shown how it's possible to perturb facial recognition with patterned eyeglass frames. Researchers have developed patterned eyeglass frames that can trick facial-recognition algorithms into seeing someone else's face. The printed frames allowed three researchers from Carnegie Mellon to successfully dodge a facial-recognition system based on machine-learning 80 percent of the time. Using certain variants of the frames, a white male was also able to fool the algorithm into mistaking him for movie actress Milla Jovovich, while a South-Asian female tricked it into seeing a Middle Eastern male. A look at some of the best IoT and smart city projects which aim to make the lives of citizens better.