In this paper, we present our method for enabling dense SDM to run at over 90 FPS on a mobile device. Our contributions are two-fold. Drawing inspiration from the FFT, we propose a Sparse Compositional Regression (SCR) framework, which enables a significant speed up over classical dense regressors. Second, we propose a binary approximation to SIFT features. Binary Approximated SIFT (BASIFT) features, which are a computationally efficient approximation to SIFT, a commonly used feature with SDM. We demonstrate the performance of our algorithm on an iPhone 7, and show that we achieve similar accuracy to SDM.
Cite
@article{arxiv.1612.05332,
title = {Fast, Dense Feature SDM on an iPhone},
author = {Ashton Fagg and Simon Lucey and Sridha Sridharan},
journal= {arXiv preprint arXiv:1612.05332},
year = {2016}
}