Liveness detection using techniques such as eye or lip movement analysis is a detection/sensing/security feature that can ensure biological identifiers are from the proper user or target of interest and not from someone else.
As biometric authentication slowly becomes incorporated into security and law enforcement applications, increasing attention has been paid to the quality and accuracy of the technology. In an attempt to win public support and minimize misidentification and security breaches, many biometric systems now promote an additional security feature: liveness detection.
Biometrics are security technologies that use a person's unique features, such as fingerprints, face or retina and iris patterns, as a replacement for IDs, passwords and other methods of identification. To use biometric authentication, users who are enrolled in a system can be transparently identified as the person in view by comparing the current biometrics to those already in the system.
Facial recognition is improving with new 3D technology systems that can detect slight changes in a person’s features. Through advanced analytics, new facial hair, glasses and even partial obstructions of the face are less likely to prevent user identification, and progress is also being made on accurate identification in both dim and bright light and with people of all complexations. Yet while the rate of false rejections is falling and accuracy improving, the majority of the solutions can still be easily spoofed.
Spoofing biometrics is when individuals use any type of replication to convince the system they are someone else. Fingerprints have been spoofed with adhesive tape and gummies, while photos, videos, masks and makeup have all been used to defeat facial recognition. This is a documented weakness of all biometrics, and as this identification technology becomes increasingly common, these issues are impacting the confidence users have in it.
Liveness detection is a security feature that can ensure biological identifiers are from the proper user and not from someone else. Traditional forms of detections can include eye or lip movement analysis, prompted motion instructions, texture/reflection detection in video feeds or zooming motion detection. Another new technique is 3D depth analysis.