一种基于HOG-LBP特征的人脸检测方法，对于遮挡的人体非常有效。-By combining Histograms of Oriented Gradients (HOG)

and Local Binary Pattern (LBP) as the feature set, we pro-

pose a novel human detection approach capable of handling

partial occlusion. Two kinds of detectors, i.e., global de-

tector for whole scanning windows and part detectors for

local regions, are learned from the training data using lin-

ear SVM. For each ambiguous scanning window, we con-

struct an occlusion likelihood map by using the response

of each block of the HOG feature to the global detector.

The occlusion likelihood map is then segmented by Mean-

shift approach. The segmented portion of the window with

a majority of negative response is inferred as an occluded

region. If partial occlusion is indicated with high likelihood

in a certain scanning window, part detectors are applied

on the unoccluded regions to achieve the fi nal classifi ca-

tion on the current scanning window. With the help of the

augmented HOG-LBP feature and the global-part occ