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We investigate in this paper the problem of estimating human ages from gait signatures. To our knowledge, this problem has not been formally addressed in the literature. Estimating human ages at a distance has a number of potential applications, including visual surveillance and monitoring in such public places as airports, railway stations, shopping malls, and various building entrances. Motivated by the fact that human gait appearances vary between males and females even within the same age group, we learn a multilabel-guided subspace to better characterize and correlate the age and gender information of a person for estimating human age. As human ages assume only nonnegative values and existing multilabel learning techniques mainly deal with ensembles of binary classes, we devise an effective label encoding scheme to convert each age value to a binary sequence, making conventional multilabel learning suitable for our task. To better characterize human gait appearance and enhance the robustness of the proposed age estimation method, we extract a set of over-complete Gabor features including both Gabor magnitude and Gabor phase information of a gait sequence and perform multiple feature fusion to enhance the age estimation performance. Our experimental results clearly demonstrate the feasibility of using gait signatures to estimate human age and the efficacy of our proposed method.