Recognition of low-resolution face images using sparse coding of local features

M. Saad Shakeel, Kin-Man-Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

In this paper, we propose a new approach for recognition of low-resolution face images by using sparse coding of local features. The proposed algorithm extracts Gabor features from a low-resolution gallery image and a query image at different scales and orientations, then projects the features separately into a new low-dimensional feature space using sparse coding that preserves the sparse structure of the local features. To determine the similarity between the projected features, a coefficient vector is estimated by using linear regression that determines the relationship between the projected gallery and query features. On the basis of this coefficient vector, residual values will be computed to classify the images. To validate our proposed method, experiments were performed using three databases (ORL, Extended-Yale B, and CAS-PEAL-R1), which contain images with different facial expressions and lighting conditions. Experimental results show that our method outperforms various classical and state-of-the-art face recognition methods.
Original languageEnglish
Title of host publication2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PublisherIEEE
ISBN (Electronic)9789881476821
DOIs
Publication statusPublished - 17 Jan 2017
Event2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of
Duration: 13 Dec 201616 Dec 2016

Conference

Conference2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Country/TerritoryKorea, Republic of
CityJeju
Period13/12/1616/12/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Signal Processing

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