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.
Facial recognition technology is becoming increasingly prevalent in our everyday lives, with many of us using the technology every time we use our face to unlock our smartphone – a study found that we use our phones around 52 times per day. Whilst it has transformed how we access our phones, facial recognition technology is also being used in a number of industries outside of tech to improve the service that companies provide customers with. If you're a company that isn't adopting the use of facial recognition, it's time to start researching into it before you get left behind. Devices recognise their users by scanning facial features and shapes – specific contours and individual unique features help the likes of smartphones recognise users and open certain settings up on phones. For example, many banking apps now allow users to login to their internet banking through the use of their face – this, in some ways, is far safer than the previous ways of using online banking which would either include an individual code or a series of questions to answer that only the user would know.
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.
Facial recognition technology has dominated discussions in technology circles for some time now. Faced with increased surveillance in public spaces, it has become imperative for stakeholders to have some input on future deployments of these novel technologies. More importantly, the general public should have some degree of understanding of facial recognition and how it's being used today. Facial recognition is a term used to refer to technologies used to analyze and recognize faces from video recordings and still images. Advancements in image processing and AI have enabled today's computer to read even the subtlest details in the human face like eyelashes to differentiate people.
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?