Cost-sensitive subspace learning for human age estimation
This paper presents a novel cost-sensitive subspace learning approach for human age estimation using face and gait signatures. Motivated by the fact that mis-estimating the age information of a person from a facial image or gait sequence could lead to different errors, we propose in this paper two new cost-sensitive subspace learning methods for human age estimation. Our approach incorporates a cost matrix, which specifies the different error associated with mis-estimating each sample, into two popular subspace learning algorithms and devise the corresponding cost-sensitive methods, namely, cost-sensitive principal component analysis (CSPCA), and cost-sensitive locality preserving projections (CSLPP), to project high-dimensional face and gait samples into the low-dimensional subspaces derived. To uncover the relation of the projected features and the ground-truth age values, we learn a multiple linear regression function with a quadratic model for age estimation. Experimental results on the MORPH face database and the USF gait database are presented to demonstrate the efficacy of our proposed methods.