Abstract
While very promising results have been shown on face recognition related problems, age-invariant face recognition still remains a challenge. Facial appearance of a person changes over time, which results in significant intraclass variations. In order to address this problem, we propose a novel deep face recognition network called age estimation guided convolutional neural network (AE-CNN) to separate the variations caused by aging from the personspecific features which are stable. The carefully designed CNN model can learn age-invariant features for face recognition. To the best of our knowledge, this is the first attempt to use age estimation task for obtaining age-invariant features. Extensive results on two well-known public domain face aging datasets: MORPH Album 2 and CACD show the effectiveness of the proposed approach.
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