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 multi-label-guided (MLG) subspace to better characterize and correlate the age and gender information of a person for estimating his/her age. As human ages assume only nonnegative values and existing multi-label learning techniques mainly deal with ensembles of different binary classes, we devise an effective label encoding scheme to convert each age value to a binary sequence, making conventional multi-label learning suitable for our task. Our experimental results clearly demonstrate the feasibility of using gait signatures to estimate human age and the efficacy of our proposed method.
['Jiwen Lu', 'Yap-Peng Tan']
Gait-based human age estimation