Few airport terminals are known for their nimble speed, but Delta is hoping to change that perception in at least one airport with new facial recognition baggage check technology. This summer, the massive American airline is planning to introduce four self-serve bag drop machines that will verify travelers' identities through the use of facial recognition software.
The system promises to be speedier than in-person, staffed bag drop, but without sacrificing ease of service. When dropping off bags, the flier will use their passport image and their own face to prove their identity to the machines. Once that (notably quick) process is complete, they'll be able to load their bags and send them away without any more hassle.
The facial recognition bag checks will be activated at Minneapolis-St.Paul International Airport this coming summer.
Delta Will Introduce Facial Recognition for Baggage Checks
1. Facial Recognition Baggage Checks - Facial recognition technology for baggage checks may expand to other airports in the near future.
2. Self-serve Bag Drop Machines - Self-service bag drop machines using facial recognition may soon become a standard in airport terminals globally.
3. Eliminating the Need for Staffed Bag Drop - Facial recognition technology eliminates the need for airport staff to manually check bags, opening up opportunities for cost savings and improved operational efficiency.
1. Airline Industry - Facial recognition baggage checks represent a disruptive innovation opportunity for the airline industry to improve customer experience and operational efficiency.
2. Security Industry - The adoption of facial recognition technology for airport security represents a disruptive innovation opportunity for the security industry to improve the accuracy and efficiency of identity verification processes.
3. Artificial Intelligence Industry - Facial recognition technology in airports represents a disruptive innovation opportunity for the artificial intelligence industry to advance in the areas of biometric identification and machine learning.