Resolution enhancement of facial image based on topdown learning

Jeong Seon Park, Seong Whan Lee

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

8 Citations (Scopus)

Abstract

This paper proposes a new method of synthesizing a highresolution facial image from a low-resolution facial image based on top-down learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be synthesized by using the optimal coefficients for linear combination of the high-resolution prototypes. The encouraging results of the proposed method show that our method can be used to increase the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at surveillance systems.

Original languageEnglish
Title of host publication1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003
PublisherAssociation for Computing Machinery, Inc
Pages59-64
Number of pages6
ISBN (Electronic)158113780X, 9781581137804
DOIs
Publication statusPublished - 2003 Nov 2
Event1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003 - Berkeley, United States
Duration: 2003 Nov 22003 Nov 8

Other

Other1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003
CountryUnited States
CityBerkeley
Period03/11/203/11/8

Keywords

  • Face recognition
  • Face reconstruction
  • Low-resolution
  • Morphable face model

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Park, J. S., & Lee, S. W. (2003). Resolution enhancement of facial image based on topdown learning. In 1st ACM SIGMM International Workshop on Video Surveillance, IWVS 2003 (pp. 59-64). Association for Computing Machinery, Inc. https://doi.org/10.1145/982452.982460