Head and Body Orientation Estimation Using Convolutional Random Projection Forests

Donghoon Lee, Ming Hsuan Yang, Songhwai Oh

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


In this paper, we consider the problem of estimating the head pose and body orientation of a person from a low-resolution image. Under this setting, it is difficult to reliably extract facial features or detect body parts. We propose a convolutional random projection forest (CRPforest) algorithm for these tasks. A convolutional random projection network (CRPnet) is used at each node of the forest. It maps an input image to a high-dimensional feature space using a rich filter bank. The filter bank is designed to generate sparse responses so that they can be efficiently computed by compressive sensing. A sparse random projection matrix can capture most essential information contained in the filter bank without using all the filters in it. Therefore, the CRPnet is fast, e.g., it requires $0.04\;\mathrm{ms}$ to process an image of $50\times 50$ pixels, due to the small number of convolutions (e.g., 0.01 percent of a layer of a neural network) at the expense of less than 2 percent accuracy. The overall forest estimates head and body pose well on benchmark datasets, e.g., over 98 percent on the HIIT dataset, while requiring $3.8\;\mathrm{ms}$ without using a GPU. Extensive experiments on challenging datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in low-resolution images with noise, occlusion, and motion blur.

Original languageEnglish
Article number8219761
Pages (from-to)107-120
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number1
Publication statusPublished - 2019 Jan 1

Bibliographical note

Funding Information:
The work of D. Lee and S. Oh is supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B2006136) and ‘The Cross-Ministry Giga KOREA Project’ grant funded by the Korea government (MSIT) (No. GK17P0300, Real-Time 4D Reconstruction of Dynamic Objects for Ultra-Realistic Services). The work of M.-H. Yang is supported in part by the NSF CAREER grant #1149783, and gifts from Adobe and Nvidia.

Publisher Copyright:
© 1979-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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