Hallucinating Facial Image Based on Adaptive Neighbourhood Selection

DOI

10.2991/iea-15.2015.60How to use a DOI?

Keywords

face hallucination; face resolution; adaptive manifold learning.

Abstract

In most manifold learning based face hallucination algorithms, the nearest neighbourhood metric is often adopted to describe the face subspace, which could not accurately capture the local geometrical structures of the samples. In this paper, a novel face superresolution approach based on adaptive neighbourhood selection is presented, which can adaptively select the nearest neighbours for each sample point. The corresponding neighbours of sample points well reflect the local geometrical structure of the face manifold, so that the linear subspace determined by the optimal linear fitting can approximate the local geometry well. Experimental results show that our method is more effective than other manifold learning based strategies for super-resolving face images.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liu Fang
AU  - Deng Yu
PY  - 2015/09
DA  - 2015/09
TI  - Hallucinating Facial Image Based on Adaptive Neighbourhood Selection
BT  - Proceedings of the AASRI International Conference on Industrial Electronics and Applications (2015)
PB  - Atlantis Press
SP  - 245
EP  - 248
SN  - 2352-5401
UR  - https://doi.org/10.2991/iea-15.2015.60
DO  - 10.2991/iea-15.2015.60
ID  - Fang2015/09
ER  -