Once data is captured, the facial recognition software can detect the face in images or in videos, and the data is matched/verified with the biometric data in the database and if data from the two sources match, a face is successfully identified
In facial recognition technology, the facial details of a person are captured. Once captured, this info is stored in a secure database of the facial recognition software. This biometric data consists of unique attributes of an individual face like the spacing of the eyes, bridge of the nose, lip contours, chin contours, among others
This stored biometric data is recognizable and verifiable by the software and once any face is detected by the facial recognition software, the details of the captured face are matched with the biometric data stored within the software database to find a match. Once data is matched by the software, a face is recognized by the system
Facial recognition technology has become popular since it is a non-intrusive (touchless) process to identify any individual. Individuals don’t have to go through a process of punching their thumb or any figure on any sensor to detect their fingerprints or even an iris scan of their eyes for the purpose of identification. This non-intrusive technology is used both for the purpose of identification of any individual and the subsequent verification of the identification by authenticating the identity
Attendance App
Axonator uses this powerful technology in its attendance management app, where facial recognition technology is used to let employees mark their attendance without having to touch any surface or punch any machine. Such a non-intrusive method to record attendance is highly functional and in great demand, especially in the on-going pandemic
Visitor Management App
Facial recognition technology is also used in our visitor management app, where it is used in access control measures for any premises, especially in offices, public buildings, and others. The visitor management system uses the face recognition technology to identify people, track their movements in the premises, bar entry for unruly or at-risk individuals to enhance the security of a building, and protect the health of employees working there
A false negative error occurs when the facial recognition system fails to identify the individual, even though biometric information of that person is present in the database, and yet the system may not be able to detect a match, due to the angle of the captured image or the use of anti-facial recognition technology
False-negative errors can be tolerated in systems where facial recognition is used, for example in a smartphone unlocking system. It is better that your phone fails to recognize your face and grant access to your phone, instead of granting access to any random system due to an error
A false positive error occurs when the system mismatches the biometric data, associating a person with someone else. A false positive error rate should be minimum in systems where facial recognition technology is being used for purposes like security or law enforcement
