Kernel entropy component analysis using local mean-based k-nearest centroid neighbour (LMKNCN) as a classifier for face recognition in video surveillance camera systems

Sepehr Damavandinejadmonfared*

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

In this paper, a new method for face recognition in video surveillance is proposed. Local mean-based k-nearest centroid neighbour (LMKNCN) is a recently proposed method for classifying data which has been proven to be more appropriate than other classifiers such as k-nearest neighbour (KNN), K-Nearest Centroid Neighbour (KNCN), and local mean-based k-nearest neighbour (LMKNN). Kernel Entropy Component Analysis is a new extension of 1-D PCA-based feature extractions methods enhancing the performance of PCA-based methods. In the proposed method in this paper, LMKNCN is used as a classifier in KPCA method. Moreover, the Extensive experiments on surveillance camera faces database (SCfaces) and Head Pose Image database reveal the significance of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
Pages253-256
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012 - Cluj-Napoca
Duration: 30 Aug 20121 Sep 2012

Other

Other2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
CityCluj-Napoca
Period30/08/121/09/12

Keywords

  • Biometrics
  • Face recognition
  • Kernel Entropy Component Analysis (KECA)
  • local mean-based k-nearest neighbor (LMKNN)
  • Principal component Analysis

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