  Facial feature tracking is essential in imaging ballistocardiography for
accurate heart rate estimation and enables motor degradation quantification in
Parkinson's disease through skin feature tracking. While deep convolutional
neural networks have shown remarkable accuracy in tracking tasks, they
typically require extensive labeled data for supervised training. Our proposed
pipeline employs a convolutional stacked autoencoder to match image crops with
a reference crop containing the target feature, learning deep feature encodings
specific to the object category in an unsupervised manner, thus reducing data
requirements. To overcome edge effects making the performance dependent on crop
size, we introduced a Gaussian weight on the residual errors of the pixels when
calculating the loss function. Training the autoencoder on facial images and
validating its performance on manually labeled face and hand videos, our Deep
Feature Encodings (DFE) method demonstrated superior tracking accuracy with a
mean error ranging from 0.6 to 3.3 pixels, outperforming traditional methods
like SIFT, SURF, Lucas Kanade, and the latest transformers like PIPs++ and
CoTracker. Overall, our unsupervised learning approach excels in tracking
various skin features under significant motion conditions, providing superior
feature descriptors for tracking, matching, and image registration compared to
both traditional and state-of-the-art supervised learning methods.
