Object representation based on gabor wave vector binning: An application to human head pose detection

Visual object recognition is a hard computer vision problem. In this paper, we investigate the issue of the representative features for object detection and propose a novel discriminative feature sets that are extracted by accumulating magnitudes for a set of specific Gabor wave vectors in 1-D histogram defined over a uniformly-spaced grid. A case study is presented using radial-basis-function kernel SVM as base learners of human head poses. In which, we point out the effectiveness of the proposed descriptors, relative to related approaches. The average performance reached 65% for yaw and 73.3% for pitch, which are better than the (40.7% and 59.0%) accuracy achieved by calibrated people. A substantial performance gain as higher as (1.18% for yaw and 1.27% for pitch) is achievable with the proposed feature sets.