EWGP: entropy-weighted Gabor and phase feature description for head pose estimation

Xiao meng Wang; Kang Liu; Ting Wang; Xu Qian

doi:10.1117/12.2243832

29 August 2016 EWGP: entropy-weighted Gabor and phase feature description for head pose estimation

Xiao meng Wang, Kang Liu, Ting Wang, Xu Qian

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 1003313 (2016) https://doi.org/10.1117/12.2243832
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Estimating focus of attention of individuals highly depends on head pose. This paper proposes an entropy weighted Gabor-phase feature description (EWGP) for head pose estimation. Gabor features represent robustness and invariability in different orientation and illuminance. However, this is not enough to express the amplitude character in images. Instead, phase congruency functions well in amplitude expression. Both illuminance and amplitude vary in terms of different regions. We regard entropy information as vote to evaluate the two aforementioned features. More specifically, entropy is represented for the randomness and content of information. We aim to utilize entropy as weight information, to fuse Gabor and phase matrix in every region. The proposed EWGP represents dramatically different when comparing to other feature matrix in datasets Pointing04. Experimental results demonstrates our case is superior to state of the art feature matrix.

Citation Download Citation

Xiao meng Wang, Kang Liu, Ting Wang, and Xu Qian "EWGP: entropy-weighted Gabor and phase feature description for head pose estimation", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003313 (29 August 2016); https://doi.org/10.1117/12.2243832

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