  The goal of this work is to demonstrate the use of the ballistocardiogram
(BCG) signal, derived using head-mounted wearable devices, as a viable
biometric for authentication. The BCG signal is the measure of an person's body
acceleration as a result of the heart's ejection of blood. It is a
characterization of the cardiac cycle and can be derived non-invasively from
the measurement of subtle movements of a person's extremities. In this paper,
we use several versions of the BCG signal, derived from accelerometer and
gyroscope sensors on a Smart Eyewear (SEW) device, for authentication. The
derived BCG signals are used to train a convolutional neural network (CNN) as
an authentication model, which is personalized for each subject. We evaluate
our authentication models using data from 12 subjects and show that our
approach has an equal error rate (EER) of 3.5% immediately after training and
13\% after about 2 months, in the worst case. We also explore the use of our
authentication approach for people with motor disabilities. Our analysis using
a separate dataset of 6 subjects with non-spastic cerebral palsy shows an EER
of 11.2% immediately after training and 21.6% after about 2 months, in the
worst-case.
