Adaptive biometric systems based on template update

Personal recognition through biometrics is focused on storing of the so-called client’s “template”, namely, the measurement, or feature, set which denotes the uniquess of his biometric with respect to those of other persons.

However, the client’s biometrics are subject to temporary or temporal variations: for example, a scratch on the fingertip may temporary change the ridge and valleys flow, as well as the lighting conditions may temporary vary during person’s acquisition over time. Moreover, some biometrics are prone to the “aging” phenomenon, as the face, which is subject to permanent, continuous slight variations over time. This causes a simple fact: the template acquired at a certain time is no more representative of the subject’s identity.

In order to reduce such effect, PRA Lab developed several algorithms aimed to automatic template update, called “self-“ and “co-“ update. Such algorithms have been developed for both mono- and multimodal systems. On this topic, PRA Lab is one of the most important group worldwide.

Thanks to these algorithms, the template set stored into the system “gallery” can be updated or renewed without or with the partial intervention of the external supervisor.


Overview of the template self-update algorithms designed and developed at PRA Lab.