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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.