Considering human ageing has a big impact on cross-age face recognition, and the effect of ageing on face recognition in non-ideal images has not been well addressed yet. In this study, the authors propose a discrimin...
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Considering human ageing has a big impact on cross-age face recognition, and the effect of ageing on face recognition in non-ideal images has not been well addressed yet. In this study, the authors propose a discriminative common feature subspace learning method to deal with the problem. Specifically, they consider the samples of the same individual with big age gaps have different distributions in the original space, and employ the maximum mean discrepancy as the distance measure to compute the distances between the sample means of the different distributions. Then the distance measure is integrated into Fisher criterion to learn a discriminative common feature subspace. The aim is to map the images with different ages to the common subspace, and to construct new feature representation which is robust to age variations and discriminative to different subjects. To evaluate the performance of the proposed method on cross-age face recognition, the authors construct extensive experiments on cacd and FG-Net databases. Experimental results show that the proposed method outperforms other subspace based methods and state-of-art cross-age face recognition methods.
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