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.
|Title of host publication||2015 International Conference on Computer Vision, ICCV 2015|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|Publication status||Published - 2015 Feb 17|
|Event||15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile|
Duration: 2015 Dec 11 → 2015 Dec 18
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Volume||2015 International Conference on Computer Vision, ICCV 2015|
|Other||15th IEEE International Conference on Computer Vision, ICCV 2015|
|Period||15/12/11 → 15/12/18|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition