Augmented Sparse Representation Classifier for Blurred Face Recognition

Jinane Mounsef, Lina Karam

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

1 Scopus citations

Abstract

The sparse representation classifier (SRC) has been developed to offer a formulation to the face recognition problem under scene dependent conditions, such as illumination/pose variations, occlusion, and disguise. However, this method has not considered image quality degradations resulting from capture, such as blur, under the same scene variations. In this work, we explore the performance of the well-known face recognition framework SRC in the presence of Gaussian blur in both constrained and unconstrained environments. Finally, we propose an augmented SRC (ASRC) framework to improve the performance of the original SRC in the presence of Gaussian blur, while preserving its robustness to scene dependent variations.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages778-782
Number of pages5
ISBN (Electronic)9781479970612
DOIs
StatePublished - Aug 29 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: Oct 7 2018Oct 10 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
CountryGreece
CityAthens
Period10/7/1810/10/18

Keywords

  • Gaussian blur
  • SRC
  • Unconstrained face recognition
  • VGG-Face

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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