Ordinary Preserving Manifold Analysis for Human Age and Head Pose Estimation

Abstract

We propose in this paper an ordinary preserving manifold analysis approach for human age and head pose estimation. While a large number of manifold learning algorithms have been proposed in the literature and some of them have been successfully applied to age/pose estimation, the ordinary characteristics of the age/pose information of samples have not been fully exploited to learn the low-dimensional discriminative features for these estimation tasks. To address this, we propose an ordinary preserving manifold analysis approach to seek a low-dimensional subspace such that the samples with similar label values (i.e., small age/pose difference) are projected to be as close as possible and those with dissimilar label values (i.e., large age/pose difference) as far as possible, simultaneously. Subsequently, we learn a multiple linear regression model to uncover the relation of these low-dimensional features and the ground-truth values of samples for age/pose estimation. Experimental results on facial age estimation, gait-based human age estimation, and head pose estimation are presented to demonstrate the efficacy of our proposed approach.