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Scale-Varying Triplet Ranking with Classification Loss for Facial Age Estimation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11365)

Abstract

In recent years, considerable efforts based on convolutional neural networks have been devoted to age estimation from face images. Among them, classification-based approaches have shown promising results, but there has been little investigation of age differences and ordinal age information. In this paper, we propose a ranking objective with two novel schemes jointly performed with an age classification objective to take ordinal age labels into account. We first introduce relative triplet sampling in which a set of triplets is constructed considering the relative differences in ages. This also addresses the problem of having limited triplet candidates, that occurs in conventional triplet sampling. We then propose the scale-varying ranking constraint, which decides the importance of a relative triplet and adjusts a scale of gradients accordingly. Our adaptive ranking loss with relative sampling not only lowers the generalization error but ultimately has a meaningful performance improvement over the state-of-the-art methods on two well-known benchmarks.

Keywords

Age estimation Triplet ranking Joint loss Deep learning 

Supplementary material

484520_1_En_16_MOESM1_ESM.pdf (435 kb)
Supplementary material 1 (pdf 434 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.KAISTDaejeonRepublic of Korea
  2. 2.SK T-BrainSeoulRepublic of Korea

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