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)


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.


Kinship verification Transfer metric learning Cross domain Sparse representation 



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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