Abstract
Security concerns increase as the technology for falsification
advances. There are strong evidences that a difficult to falsify
biometric trait, the human heartbeat, can be used for identity
recognition. Existing solutions for biometric recognition
from electrocardiogram (ECG) signals are based on temporal
and amplitude distances between detected fiducial points.
Such methods rely heavily on the accuracy of fiducial detection,
which is still an open problem due to the difficulty in
exact localization of wave boundaries. This paper presents a
systematic analysis for human identification from ECG data.
A fiducial-detection-based framework that incorporates analytic
and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point
only. Further, to completely relax the detection of fiducial
points, a new approach based on autocorrelation (AC) in conjunction
with discrete cosine transform (DCT) is proposed.
Experimentation demonstrates that the AC/DCT method produces
comparable recognition accuracy with the fiducial-detection-based approach.