Head and Body Orientation Estimation Using Convolutional Random Projection Forests
In this paper, we consider the problem of estimating the head pose and body orientation of a person from a low-resolution image. Under this setting, it is difficult to reliably extract facial features or detect body parts. We propose a convolutional random projection forest (CRPforest) algorithm for these tasks. A convolutional random projection network (CRPnet) is used at each node of the forest. It maps an input image to a high-dimensional feature space using a rich filter bank. The filter bank is designed to generate sparse responses so that they can be efficiently computed by compressive sensing. A sparse random projection matrix can capture most essential information contained in the filter bank without using all the filters in it. Therefore, the CRPnet is fast, e.g., it requires <inline-formula><tex-math notation="LaTeX">$0.04\;\mathrm{ms}$</tex-math><alternatives> <inline-graphic xlink:href="yang-ieq1-2784424.gif"/></alternatives></inline-formula> to process an image of <inline-formula><tex-math notation="LaTeX">$50\times 50$</tex-math><alternatives> <inline-graphic xlink:href="yang-ieq2-2784424.gif"/></alternatives></inline-formula> pixels, due to the small number of convolutions (e.g., 0.01 percent of a layer of a neural network) at the expense of less than 2 percent accuracy. The overall forest estimates head and body pose well on benchmark datasets, e.g., over 98 percent on the HIIT dataset, while requiring <inline-formula><tex-math notation="LaTeX">$3.8\;\mathrm{ms}$</tex-math><alternatives> <inline-graphic xlink:href="yang-ieq3-2784424.gif"/></alternatives></inline-formula> without using a GPU. Extensive experiments on challenging datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in low-resolution images with noise, occlusion, and motion blur.