Fusion of locally linear embedding and principal component analysis for face recognition (FLLEPCA)

Eimad Eldin Abusham, David Ngo, Beng Jin Teoh

Research output: Contribution to journalConference article

14 Citations (Scopus)

Abstract

We proposed a novel approach for face recognition to address the challenging task of recognition using a fusion of nonlinear dimensional reduction; Locally Linear Embedding (LLE) and Principal Component Analysis (PCA). LLE computes a compact representation of high dimensional data combining the major advantages of linear methods, With the advantages of nonlinear approaches which is flexible to learn a broad of class on nonlinear manifolds. The application of LLE, however, is limited due to its lack of a parametric mapping between the observation and the low-dimensional output. In addition, the revealed underlying manifold can only be observed subjectively. To overcome these limitations, we propose our method for recognition by fusion of LLE and Principal Component Analysis (FLLEPCA) and validate their efficiency. Experiments on CMU AMP Face Expression Database and JAFFE databases show the advantages of our proposed novel approach.

Original languageEnglish
Pages (from-to)326-333
Number of pages8
JournalLecture Notes in Computer Science
Volume3687
Issue numberPART II
Publication statusPublished - 2005 Nov 4
EventThird International Conference on Advances in Patten Recognition, ICAPR 2005 - Bath, United Kingdom
Duration: 2005 Aug 222005 Aug 25

Fingerprint

Locally Linear Embedding
Face recognition
Face Recognition
Principal component analysis
Principal Component Analysis
Fusion
Fusion reactions
Dimensional Reduction
High-dimensional Data
Experiments
Face
Output
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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abstract = "We proposed a novel approach for face recognition to address the challenging task of recognition using a fusion of nonlinear dimensional reduction; Locally Linear Embedding (LLE) and Principal Component Analysis (PCA). LLE computes a compact representation of high dimensional data combining the major advantages of linear methods, With the advantages of nonlinear approaches which is flexible to learn a broad of class on nonlinear manifolds. The application of LLE, however, is limited due to its lack of a parametric mapping between the observation and the low-dimensional output. In addition, the revealed underlying manifold can only be observed subjectively. To overcome these limitations, we propose our method for recognition by fusion of LLE and Principal Component Analysis (FLLEPCA) and validate their efficiency. Experiments on CMU AMP Face Expression Database and JAFFE databases show the advantages of our proposed novel approach.",
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Fusion of locally linear embedding and principal component analysis for face recognition (FLLEPCA). / Abusham, Eimad Eldin; Ngo, David; Teoh, Beng Jin.

In: Lecture Notes in Computer Science, Vol. 3687, No. PART II, 04.11.2005, p. 326-333.

Research output: Contribution to journalConference article

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