Supervised Locally Linear Embedding in face recognition

Ying Han Pang, Andrew Beng Jin Teoh, Eng Kiong Wong, Fazly Salleh Abas

Research output: Contribution to conferencePaperpeer-review

25 Citations (Scopus)


Locally Linear Embedding (LLE), which has recently emerged as a powerful face feature descriptor, suffers from a limitation. That is class-specific information of data is lacked of during face analysis. Thus, we propose a supervised LLE technique, known as class-label Locally Linear Embedding (cLLE), to overcome the problem. cLLE is able to discover the nonlinearity of high-dimensional face data by minimizing the global reconstruction error of the set of all local neighbors in the data set. cLLE utilizes user class-specific information in neighborhoods selection and thus preserves the local neighborhoods. Since the locality preservation is correlated to the class discrimination, the proposed cLLE is expected superior to LLE in face recognition. Experimental results on three face databases: ORL, AR and Yale databases, demonstrate that the proposed technique obtains better recognition performance than PCA and LLE.

Original languageEnglish
Publication statusPublished - 2008
EventIEEE- International Symposium on Biometrics and Security Technologies, ISBAST'08 - Islamabad, Pakistan
Duration: 2008 Apr 232008 Apr 24


OtherIEEE- International Symposium on Biometrics and Security Technologies, ISBAST'08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Control and Systems Engineering


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