An efficient multiscale scheme using local Zernike moments for face recognition

Emrah Basaran*, Muhittin Gökmen, Mustafa E. Kamasak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.

Original languageEnglish
Article number827
JournalApplied Sciences (Switzerland)
Volume8
Issue number5
DOIs
Publication statusPublished - 21 May 2018

Bibliographical note

Publisher Copyright:
© 2018 by the authors.

Keywords

  • Face identification
  • Face recognition
  • Face verification
  • Local Zernike moments
  • Local descriptors

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