EWGP: entropy-weighted Gabor and phase feature description for head pose estimation
Xiao Meng Wang, Kang Liu, Ting Wang, Qian Xu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
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.