Random Projection Forest

This MATLAB package includes the implementation of the random projection forest with an application of head pose estimation. 

Fast and Accurate Head Pose Estimation via Random Projection Forests

  • Article: Donghoon Lee, Ming-Hsuan Yang, and Songhwai Oh, "Fast and Accurate Head Pose Estimation via Random Projection Forests," in Proc. of the IEEE International Conference on Computer Vision (ICCV), Dec. 2015.
  • Abstract: In this paper, we consider the problem of estimating the gaze direction of a person from a low-resolution image. Under this condition, reliably extracting facial features is very difficult. We propose a novel head pose estimation algorithm based on compressive sensing. Head image patches are mapped to a large feature space using the proposed extensive, yet efficient filter bank. The filter bank is designed to generate sparse responses of color and gradient information, which can be compressed using random projection, and classified by a random forest. Extensive experiments on challenging datasets show that the proposed algorithm performs favorably against the state-of-the-art methods on head pose estimation in low-resolution images degraded by noise, occlusion, and blurring.
  • Bibtex entry: 
@inproceedings {lee:RandomProjectionForest:iccv15,
  author    = {Donghoon Lee and Ming-Hsuan Yang and Songhwai Oh},
  title     = {Fast and Accurate Head Pose Estimation via Random Projection Forests}, 
  bocktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  month     = {December},
  year      = {2015}
}

DEMO

This example is provided in headpose.m. There are three steps written below.

  1. Setting parameters and experimental environments.
  2. Train random projection forest using rpfTrain.m
  3. Test random projection forest using rpfTest.m

Download

This software is made available for free for non-commercial use. The software must not be modified or distributed without prior permission of the author. Please send your request to webmaster@cpslab.snu.ac.kr. In your email, please include your name and institution. By submitting this request you agree to be bound by this license.

Current version: 0.2, Feburay 2016