2016/12/16 by Ashton Fagg, Simon Lucey, Fagg, Ashton +3
Computer Science · Engineering · Mathematics · #Advanced Image and Video Retrieval Techniques #Algorithm #Arithmetic #Artificial intelligence #Binary number #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #Face and Expression Recognition #Fast Fourier transform #Feature (linguistics) #Feature extraction #Mathematics #Pattern recognition (psychology) #Scale-invariant feature transform #Sparse and Compressive Sensing Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1612.05332
published in arXiv (Cornell University) (Cornell University)
arxiv created 2016/12/16 · openalex publication_date 2016/12/16 · arxiv updated 2016/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.