MasterCard Tests Facial Scans For Mobile Payments

Millennials: Why Customer Service Will Never Be The Same

Will selfies replace fingerprints and passwords for authentication? One of the nation's largest credit card providers is taking the leap into facial recognition technology for mobile payments.

MasterCard plans to begin experimenting this fall with facial scans as an added security measure for consumers paying for purchases with a smartphone. "The new generation, which is into selfies ... I think they'll find it cool. They'll embrace it," said Ajay Bhalla, MasterCard's president of enterprise security solutions, in an interview with CNN Money.

A pilot program will be rolled out to 500 users of the company's mobile app this fall. Major smartphone makers, including Apple, Samsung, Google, Microsoft, and BlackBerry are reportedly on board, and carriers are expected to be announced soon.

Users will have the option of a fingerprint or facial scan. The facial recognition program will map out the user's face and convert it to binary code, which is then transmitted over the Web to MasterCard. Once the scan is set up, a user will see their image prior to making a purchase and will have to blink at their smartphone in order to verify their identity and complete the purchase. Bhalla explained how it works in this video.

[ What's next, mind-reading? Read Why Facebook, Telepathy Don't Mix. ]

While coming up with secure authentication options to the password is a priority for many companies, some security specialists don't think MasterCard's plan is a great idea. Robert Lee, the co-founder of consulting firm Dragos Security, told CNN Money: "From a privacy aspect it's awful – but from a business perspective, I don't understand why they'd accept that risk."

Yet, the use of biometrics is poised to grow, according to a June report from Tractica, which estimates that the number of facial recognition devices and licenses sold will increase from 28.5 million in 2015 to more than 122.8 million worldwide by 2024. During that period, annual revenue from sales of facial biometrics – which includes visible light facial recognition and infrared-based facial thermographs – is predicted to increase from nearly $150 million to nearly $883 million, at a compound annual growth rate (CAGR) of 22%.

"Springing from technological breakthroughs in the 1980s and 1990s, facial recognition is currently experiencing a period of growth as cost curves and use cases converge to produce an increasing variety of applications in consumer, enterprise, and government markets worldwide," the Tractica report said. "Adoption of facial recognition will be particularly strong during the next several years in mobile device authentication, but areas of growth will also include government applications such as national ID cards and biometrics passports, as well as finance/banking and retail applications."

The report examined 20 use cases specific to face biometrics, forecasting revenue and software licenses for facial recognition, along with revenue and device shipments for facial thermography.

"The largest use case for facial recognition is mobile device authentication," Tractica principal analyst Bob Lockhart said in a statement. "This use case is defined in massive volumes but at a small unit price. The next substantial use case for facial recognition is to identify persons of interest. This use case exists in government, defense, law enforcement, and enterprise markets. Future applications include anonymously profiling people for uses like customized digital signage."

Such use cases are raising significant concerns among privacy advocates, particularly when it comes to use of facial recognition in the retail sector.

The pitfalls of facial recognition technology extend beyond security and privacy concerns, however. For example, Google offered profuse apologies after a recent incident in which African-Americans were labeled as gorillas through Google's facial recognition software. "We're appalled and genuinely sorry that this happened," a Google spokeswoman told the BBC. "We are taking immediate action to prevent this type of result from appearing. There is still clearly a lot of work to do with automatic image labeling, and we're looking at how we can prevent these types of mistakes from happening in the future."