DWT and sub-pattern PCA for face recognition based on fuzzy data fusion

Yang Ting Chou, Shih Ming Huang, Szu Hua Wu, Jar-Ferr Yang

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

1 Citation (Scopus)

Abstract

In realistic situation, the outlier could affect the face recognition rate severely. To overcome this problem, we propose a novel face recognition system to improve the recognition rate. The system can be divided into three aspects. Firstly, the 2D discrete wavelet transform (2D-DWT) is used for noise removal. Secondly, we use the principle component analysis (PCA) to extract features. In fact, the feature information from global face is not so robust that we intend to extract the local features, called the sub-pattern PCA (sp-PCA). Thirdly, we introduce an improved fuzzy fusion algorithm called adaptive membership grade to improve the ability of similar data separation. The experimental results show that the proposed system reveals better recognition rate.

Original languageEnglish
Title of host publicationProceedings - 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011
Pages296-299
Number of pages4
DOIs
Publication statusPublished - 2011 Dec 1
Event2011 IEEE International conference on Intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011 - Wuhan, Hubei, China
Duration: 2011 Dec 142011 Dec 17

Publication series

NameProceedings - 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

Other

Other2011 IEEE International conference on Intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011
CountryChina
CityWuhan, Hubei
Period11-12-1411-12-17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Biomedical Engineering

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