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.

中文翻译：

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区分性共同特征子空间学习用于年龄不变的人脸识别

考虑到人类衰老对跨时代的人脸识别有很大的影响，而衰老对非理想图像中人脸识别的影响尚未得到很好的解决。在这项研究中，作者提出了一种区分性的共同特征子空间学习方法来解决该问题。具体来说，他们认为年龄差距较大的同一个人的样本在原始空间中具有不同的分布，并采用最大平均差异作为距离度量来计算不同分布的样本均值之间的距离。然后将距离度量集成到Fisher准则中，以学习判别性公共特征子空间。目的是将不同年龄的图像映射到公共子空间，并构造新的特征表示法，以适应年龄变化，并能区分不同的主题。为了评估该方法在跨年龄人脸识别中的性能，作者在CACD和FG-Net数据库上进行了广泛的实验。实验结果表明，该方法优于其他基于子空间的方法和最新的跨年龄人脸识别方法。