Digital images of our face, eyes, irises, and other physical features can be used as “biometrics” to digitally verify our identity. In this way, our biometrics can be used with a computing device such as a smart phone in a way that is analogous to passwords, potentially enabling identity verification with more security and convenience than with the use of passwords. For example, a user might use his or her facial image as a biometric in place of (or in addition to) a password in order to log in to an application running on a mobile phone.
In the example of biometric identity verification using facial images, a user captures a “selfie” image of his or her face using the screen-side camera as part of an “enrollment” process. The image is analyzed to locate and quantify special features, which are then securely stored. Upon identity “verification,” the process is repeated and the quantified features of the image are compared to those produced from the enrollment process. In this way, biometric capture and matching algorithms can be used to assess the likelihood that the biometric samples are derived from the same person.
In applications where biometric identity verification is used to protect sensitive or valuable information, there can be a vulnerability to “spoofing,” where a “fraudster” attempts to commit a crime by impersonating their victim, such as to gain access to their online bank accounts through their mobile banking application. Such fraud attempts are referred to as “presentation attacks,” where fraud is attempted by falsely presenting the identity of a victim upon verification. Such a presentation attack might be attempted by presenting a photograph or digital video of the targeted victim in place of their live image.
To address this vulnerability, biometric systems apply “spoof detection”, or “likeness detection” techniques designed to detect presentation attack attempts. Many of these approaches require interaction with the user, such as by displaying commands to the user such as “blink” or “turn head”. Software is used to analyze the image stream to detect the subject activity and compare it to expected behavior.