Supervised-learning based face hallucination for enhancing face recognition

Weng Tai Su, Chih Chung Hsu, Chia Wen Lin, Weiyao Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This paper presents a two-step supervised face hallucination framework based on class-specific dictionary learning. Since the performance of learning-based face hallucination relies on its training set, an inappropriate training set (e.g., an input face image is very different from the training set) can reduce the visual quality of reconstructed high-resolution (HR) face significantly. To address this problem, we propose to utilize supervised learning to learn a set of class-specific dictionaries so that one of the learned dictionaries can well fit the global and local characteristics of an input low-resolution (LR) face image. Besides, the representative coefficients of the input LR face image may be unreliable due to insufficient information contained in the LR input image. To resolve this issue, we propose a maximum a posteriori estimator to infer the global HR face. Experimental results demonstrate that our method cannot only effectively enhance the visual quality of a reconstructed HR face, but also significantly improves the accuracy of face recognition compared to existing hallucination methods.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1751-1755
Number of pages5
ISBN (Electronic)9781479999880
DOIs
Publication statusPublished - 2016 May 18
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: 2016 Mar 202016 Mar 25

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period16-03-2016-03-25

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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