Label Distribution Learning Based Age-Invariant Face Recognition

Face recognition is an important application of computer vision. Al-though the accuracy of face recognition is high, face recognition and retrieval across age is still challenging. Faces across age can be very different caused by the aging process over time. The problem is that the images are not too similar, but with the same label. To reduce the intraclass discrepancy, in this paper we pro-pose a new method called Label Distribution learning for the end-to-end neural network to learn more discriminative features. Extensive experiments conducted on the three public domain face aging datasets (MORPH Album 2, CACD-VS and LFW) have shown the effectiveness of the proposed approach.

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