Entropy-weighted feature-fusion method for head-pose estimation

This paper proposes a novel entropy-weighted Gabor-phase congruency (EWGP) feature descriptor for head-pose estimation on the basis of feature fusion. Gabor features are robust and invariant to differences in orientation and illuminance but are not sufficient to express the amplitude character in images. By contrast, phase congruency (PC) functions work well in amplitude expression. Both illuminance and amplitude vary over distinctive regions. Here, we employ entropy information to evaluate orientation and amplitude to execute feature fusion. More specifically, entropy is used to represent the randomness and content of information. For the first time, we seek to utilize entropy as weight information to fuse the Gabor and phase matrices in every region. The proposed EWGP feature matrix was verified on Pointing’04 and FacePix. The experimental results demonstrate that our method is superior to the state of the art in terms of MSE, MAE, and time cost.