Occluded Face Recognition Using Sparse Complex Matrix Factorization with Ridge Regularization

Diyah Utami Kusumaning Putri, Aina Musdholifah, Faizal Makhrus, Viet Hang Duong, Phuong Thi Le, Bo Wei Chen, Jia Ching Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Matrix factorization is a method for dimensionality reduction which plays an important role in pattern recognition and data analysis. This work exploits the usefulness of our proposed complex matrix factorization (CMF) with ridge regularization (SCMF-L2) in occluded face recognition. Experiments on occluded face recognition reveal that the SCMF-L2 method provides the best recognition result among all the nonnegative matrix factorization (NMF) and CMF methods. The proposed method also reaches the stopping condition and converge much faster than the other NMF and CMF methods.

Original languageEnglish
Title of host publicationISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems
Subtitle of host publication5G Dream to Reality, Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665419512
DOIs
StatePublished - 2021
Event2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021 - Hualien, Taiwan
Duration: 16 Nov 202119 Nov 2021

Publication series

NameISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding

Conference

Conference2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021
Country/TerritoryTaiwan
CityHualien
Period16/11/2119/11/21

Keywords

  • Complex matrix factorization
  • Nonnegative matrix factorization
  • Occluded face recognition

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