Facial Recognition with Encoded Local Projections

Dhruv Sharma, Sarim Zafar, Morteza Babaie, H. R. Tizhoosh

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

Abstract

Encoded Local Projections (ELP) is a recently introduced dense sampling image descriptor which uses projections in small neighbourhoods to construct a histogram/descriptor for the entire image. ELP has shown to be as accurate as other state-of-the-art features in searching medical images while being time and resource efficient. This paper attempts for the first time to utilize ELP descriptor as primary features for facial recognition and compare the results with LBP histogram on the Labeled Faces in the Wild dataset. We have evaluated descriptors by comparing the chi-squared distance of each image descriptor versus all others as well as training Support Vector Machines (SVM) with each feature vector. In both cases, the results of ELP were better than LBP in the same sub-image configuration.

Original languageEnglish (US)
Title of host publicationProceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
EditorsSuresh Sundaram
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1261-1268
Number of pages8
ISBN (Electronic)9781538692769
DOIs
StatePublished - Jan 28 2019
Event8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India
Duration: Nov 18 2018Nov 21 2018

Publication series

NameProceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

Conference

Conference8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Country/TerritoryIndia
CityBangalore
Period11/18/1811/21/18

Keywords

  • Encoded Local Projections (ELP)
  • Facial Recognition
  • LBP
  • SVM

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science

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