JP Data LLC
101 Jefferson Dr, 1st Floor
Menlo Park CA 94025
Phone
(408) 623 9165
Email
info at jpdata dot co
sales at jpdata dot co
JP Data LLC
101 Jefferson Dr, 1st Floor
Menlo Park CA 94025
Phone
(408) 623 9165
Email
info at jpdata dot co
sales at jpdata dot co
Facial recognition is one of the popular applications of computer vision technology that deals with recognizing identity of a person. The technology has been in use for several years with varying degree of accuracy. Classic facial recognition techniques, such as SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), relied on extracting unique features of face. These techniques relied on comparing values of the incoming picture with a reference picture to generate a match. While this worked fairly well in situations where the incoming picture featured similar conditions (i.e. a photograph with similar background), it was generally inadequate to detect a person’s aging or partially covered face and the accuracy remained limited. Despite its limitations, facial recognition was use frequently in security and surveillance and has been used by law enforcement agencies quite some time.
With the advances of AI, new techniques for facial recognition emerged. The accuracy of results has increased drastically. The technology has advanced further to recognize facial features to detect emotions and even diseases. Historically PC class device was required to run such algorithms but the falling prices of hardware has enabled running facial recognition on camera making it easy to add new capabilities to different places. This is giving rise to wide range of new applications via the facial recognition technology.
Apple’s latest iPhone is the most prominent application of late that is being used by general users. The technology allows users to unlock the device by simply looking at the screen. Apple recently has also announced availability of face detection APIs that developers can embed in their applications. All this runs on device itself making it easy to create edge applications.
Southern California based chain, Caliburger is taking facial recognition in retail space. It is testing a facial recognition based system to order burgers. The system links loyalty customer’s face to their past orders. For subsequent orders, the customers just need to look into the camera and their orders will automatically pop up.
In Boston, department of Homeland security is testing a system that lets traveler check-in via facial recognition rather than boarding pass. For certain international flights in Atlanta and New York, DHS has partnered with Delta to bring mandatory face recognition scans to the boarding gate. The Delta system checks a passenger is supposed to be on the plane by comparing the face, captured by a kiosk at the boarding gate, to passenger manifest photos from State Department databases.
Walmart is testing a facial recognition system to identify unhappy customers. The technology uses video cameras at store checkout lines that monitor customers’ facial expressions and movements. If the behavior is deemed to be ‘unhappy’, the system alerts its employees.
Facebook has also recently launched a new facial recognition feature called Photo Review. The features alerts users when their face shows up in newly posted photos. User has an option to tag themselves, leave as is or ask the uploader to take the photo down, or even report it.
The protocol in case of failure in any of these systems is still unclear. If the burger chain’s system that recognizes face fails, it is perhaps not going to cause any problem. However if a person is denied boarding on a flight due to failure of the DHS system, it is sure to generate lot of media spotlight.
More and more places and things are now starting to recognize you
Facial recognition is one of the popular applications of computer vision technology that deals with recognizing identity of a person. The technology has been in use for several years with varying degree of accuracy. Classic facial recognition techniques, such as SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), relied on extracting unique features of face. These techniques relied on comparing values of the incoming picture with a reference picture to generate a match. While this worked fairly well in situations where the incoming picture featured similar conditions (i.e. a photograph with similar background), it was generally inadequate to detect a person’s aging or partially covered face and the accuracy remained limited. Despite its limitations, facial recognition was use frequently in security and surveillance and has been used by law enforcement agencies quite some time.
With the advances of AI, new techniques for facial recognition emerged. The accuracy of results has increased drastically. The technology has advanced further to recognize facial features to detect emotions and even diseases. Historically PC class device was required to run such algorithms but the falling prices of hardware has enabled running facial recognition on camera making it easy to add new capabilities to different places. This is giving rise to wide range of new applications via the facial recognition technology.
Apple’s latest iPhone is the most prominent application of late that is being used by general users. The technology allows users to unlock the device by simply looking at the screen. Apple recently has also announced availability of face detection APIs that developers can embed in their applications. All this runs on device itself making it easy to create edge applications.
Southern California based chain, Caliburger is taking facial recognition in retail space. It is testing a facial recognition based system to order burgers. The system links loyalty customer’s face to their past orders. For subsequent orders, the customers just need to look into the camera and their orders will automatically pop up.
In Boston, department of Homeland security is testing a system that lets traveler check-in via facial recognition rather than boarding pass. For certain international flights in Atlanta and New York, DHS has partnered with Delta to bring mandatory face recognition scans to the boarding gate. The Delta system checks a passenger is supposed to be on the plane by comparing the face, captured by a kiosk at the boarding gate, to passenger manifest photos from State Department databases.
Walmart is testing a facial recognition system to identify unhappy customers. The technology uses video cameras at store checkout lines that monitor customers’ facial expressions and movements. If the behavior is deemed to be ‘unhappy’, the system alerts its employees.
Facebook has also recently launched a new facial recognition feature called Photo Review. The features alerts users when their face shows up in newly posted photos. User has an option to tag themselves, leave as is or ask the uploader to take the photo down, or even report it.
The protocol in case of failure in any of these systems is still unclear. If the burger chain’s system that recognizes face fails, it is perhaps not going to cause any problem. However if a person is denied boarding on a flight due to failure of the DHS system, it is sure to generate lot of media spotlight.
Facial recognition technology is evolving at a rapid pace and more and more places will soon know their customers and users by face. It is unclear how this will impact human behavior and if there would be any social pushback to such system. One thing is certain that when used wisely, facial recognition can open up many new business opportunities.