Microsoft claims its facial recognition technology just got a little less awful. Earlier this year, a study by MIT researchers found that tools from IBM, Microsoft, and Chinese company Megvii could correctly identify light-skinned men with 99-percent accuracy. But it incorrectly identified darker-skinned women as often as one-third of the time. Now imagine a computer incorrectly flagging an image at an airport or in a police database, and you can see how dangerous those errors could be. Microsoft's software performed poorly in the study.
A team of engineering researchers from the University of Toronto has created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview.
American law enforcement agencies have created a massive facial recognition database. If you're an adult in the US, you might already be in it. According to a comprehensive report by the Center for Privacy & Technology at Georgetown Law, the law enforcement's database has 117 million American adults on file. The report says authorities used driver's license IDs from 26 states to build the database, which includes people who've never committed any kind of crime before. That's already a problem in and of itself, but it's compounded by the lack of oversight on how it's used.
The Facial Recognition Technology Is Known to Have Gained a Foothold in Many Industry Verticals and It Keeps on Continuously Charting New Ground. Facial Recognition has gained so much traction in an entire host of verticals and applications (according to Variant Market Research, its market is expected to be worth some $ 15.4 billion by 2024) that most anyone, regardless of the kind of business they are in, should look into whether the technology could come in handy in reaching their business objectives. In part, this is owing to the ability of the Facial Recognition technology to better equip and advance the field of expertise known as Marketing, - something universal and of the utmost importance to most industries. Moreover, Face Recognition can make a dent in precisely those areas of Marketing, in which the now rampant Digital Marketing falls short, or is, simply, irrelevant. What are those areas, how much headway has been made already and what are the potentialities one should be aware of?