Unsupervised Skin Feature Tracking with Deep Neural Networks

• URL: http://arxiv.org/abs/2405.04943v1
• Date: Wed, 8 May 2024 10:27:05 GMT
• Title: Unsupervised Skin Feature Tracking with Deep Neural Networks
• Authors: Jose Chang, Torbjörn E. M. Nordling,
• Abstract summary: Deep convolutional neural networks have shown remarkable accuracy in tracking tasks. Our pipeline employs a convolutional stacked autoencoder to match image crops with a reference crop containing the target feature. Our unsupervised learning approach excels in tracking various skin features under significant motion conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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