Abstract
Building on the recent advances in the Fisher kernel framework for image classification, this paper proposes a novel image representation for head yaw estimation. Specifically, for each pixel of the image, a concise 9-dimensional local descriptor is computed consisting of the pixel coordinates, intensity, the first and second order derivatives, as well as the magnitude and orientation of the gradient. These local descriptors are encoded by Fisher vectors before being pooled to produce a global representation of the image. The proposed image representation is effective to head yaw estimation, and can be further improved by metric learning. A series of head yaw estimation experiments have been conducted on five datasets, and the results show that the new image representation improves the current state-of-the-art for head yaw estimation.
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