Face Hallucination via Locality-Constrained Low-Rank Representation Conference Paper uri icon


  • © 2016 IEEE. Face hallucination (FH) based on sparse representation (SR) and locality-constrained representation (LCR) gives reasonably good performance. However, neither SR-nor LCR-based methods make full use of the structure information in the training data. On the other hand, low-rank representation (LRR) has been utilized to cluster samples into their respective classes by exploiting low-rank structures of the data. In this paper, we propose a locality-constrained low-rank representation (LCLRR) method to take advantage of both LCR and LRR for FH. LCLRR first enforces a low-rank constraint on choosing the dictionary atoms that belong to a subspace that correspond to the same cluster, it then imposes a locality constraint on selecting atoms that are in the vicinity of test samples. Experiments show that LCLRR outperforms both SR- and LCR-based methods on subjectively and objectively, proving that exploiting the structure information in the training data is feasible in face hallucination.

author list (cited authors)

  • Lu, T., Xiong, Z., Wan, Y., & Yang, W.

citation count

  • 3

publication date

  • March 2016