  Facial feature tracking is a key component of imaging ballistocardiography
(BCG) where accurate quantification of the displacement of facial keypoints is
needed for good heart rate estimation. Skin feature tracking enables
video-based quantification of motor degradation in Parkinson's disease.
Traditional computer vision algorithms include Scale Invariant Feature
Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method
(LK). These have long represented the state-of-the-art in efficiency and
accuracy but fail when common deformations, like affine local transformations
or illumination changes, are present.
  Over the past five years, deep convolutional neural networks have
outperformed traditional methods for most computer vision tasks. We propose a
pipeline for feature tracking, that applies a convolutional stacked autoencoder
to identify the most similar crop in an image to a reference crop containing
the feature of interest. The autoencoder learns to represent image crops into
deep feature encodings specific to the object category it is trained on.
  We train the autoencoder on facial images and validate its ability to track
skin features in general using manually labeled face and hand videos. The
tracking errors of distinctive skin features (moles) are so small that we
cannot exclude that they stem from the manual labelling based on a
$\chi^2$-test. With a mean error of 0.6-4.2 pixels, our method outperformed the
other methods in all but one scenario. More importantly, our method was the
only one to not diverge.
  We conclude that our method creates better feature descriptors for feature
tracking, feature matching, and image registration than the traditional
algorithms.
