  The expanding usage of complex machine learning methods like deep learning
has led to an explosion in human activity recognition, particularly applied to
health. In particular, as part of a larger body sensor network system, face and
full-body analysis is becoming increasingly common for evaluating health
status. However, complex models which handle private and sometimes protected
data, raise concerns about the potential leak of identifiable data. In this
work, we focus on the case of a deep network model trained on images of
individual faces. Full-face video recordings taken from 493 individuals
undergoing an eye-tracking based evaluation of neurological function were used.
Outputs, gradients, intermediate layer outputs, loss, and labels were used as
inputs for a deep network with an added support vector machine emission layer
to recognize membership in the training data. The inference attack method and
associated mathematical analysis indicate that there is a low likelihood of
unintended memorization of facial features in the deep learning model. In this
study, it is showed that the named model preserves the integrity of training
data with reasonable confidence. The same process can be implemented in similar
conditions for different models.
