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Transfer Metric Learning for Kinship Verification with Locality-Constrained Sparse Features

  • Yanli Zhang
  • Bo MaEmail author
  • Lianghua Huang
  • Hongwei Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

Abstract

Kinship verification between aged parents and their children based on facial images is a challenging problem, due to aging factor which makes their facial similarities less distinct. In this paper, we propose to perform kinship verification in a transfer learning manner, which introduces photos of parents in their earlier ages as intermediate references to facilitate the verification. Child-young parent pairs are regarded as source domain and child-old parent ones are considered as target domain. The transfer learning scheme contains two phases. In the transfer metric learning phase, the extracted locality-constrained sparse features of images are projected into an optimized subspace where the intra-class distances are minimized and the inter-class ones are maximized. In the transfer classifier learning phase, a cross domain classifier is learned by a transfer SVM algorithm. Experimental results on UB KinFace dataset indicate that our method outperforms state-of-the-art methods.

Keywords

Kinship verification Transfer metric learning Cross domain Sparse representation 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (No. 61472036).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yanli Zhang
    • 1
  • Bo Ma
    • 1
    Email author
  • Lianghua Huang
    • 1
  • Hongwei Hu
    • 1
  1. 1.Beijing Laboratory of Intelligent Information Technology, School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina

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