  Cardiac patterns are being used to obtain hard-to-forge biometric signatures
and have led to high accuracy in state-of-the-art (SoA) identification
applications. However, this performance is obtained under controlled scenarios
where cardiac signals maintain a relatively uniform pattern, facilitating the
identification process. In this work, we analyze cardiac signals collected in
more realistic (uncontrolled) scenarios and show that their high signal
variability (i.e., irregularity) makes it harder to obtain stable and distinct
user features. Furthermore, SoA usually fails to identify specific groups of
users, rendering existing identification methods futile in uncontrolled
scenarios. To solve these problems, we propose a framework with three novel
properties. First, we design an adaptive method that achieves stable and
distinct features by tailoring the filtering spectrum to each user. Second, we
show that users can have multiple cardiac morphologies, offering us a much
bigger pool of cardiac signals and users compared to SoA. Third, we overcome
other distortion effects present in authentication applications with a
multi-cluster approach and the Mahalanobis distance. Our evaluation shows that
the average balanced accuracy (BAC) of SoA drops from above 90% in controlled
scenarios to 75% in uncontrolled ones, while our method maintains an average
BAC above 90% in uncontrolled scenarios.
