Lin-Bo Cai

Publications (4)0 Total impact

• Conference Proceeding: Face Recognition with Locally Linear Embedding on Local Binary Patterns

  Lin-bo Cai, Zi-lu Ying
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  ABSTRACT: In this paper, A new approach to face recognition is constructed by combining the local binary pattern (LBP) operator and locally linear embedding (LLE). LBP is an effective low-cost image descriptor to extract facial texture feature which represents the local structure of face images. LLE is an excellent non-linear data dimensionality reduction method. Its main optimization only involves a sparse eigenvalue problem and do not involves local minima. The new approach benefits from the advantages of both LBP and LLE. The proposed algorithm is experimented on ORL database. Extensive experiments are carried out to compare with other common methods such as LDA and LLE. The experiment results show that the combination of LBP+LLE provides better performance than that of those traditional algorithms and prove the effectiveness of the proposed algorithm.
  Information Science and Engineering (ICISE), 2009 1st International Conference on; 01/2010
• Conference Proceeding: Support Vector Discriminant Analysis on Local Binary Patterns for Facial Expression Recognition

  Zi-lu Ying, Lin-bo Cai
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  ABSTRACT: In this paper, a new approach to facial expression recognition is constructed by combining the support vector discriminant analysis (SVDA) and local binary pattern (LBP) operator. LBP is an effective low-cost image descriptor to extract facial texture representing expression features. The basic idea of SVDA is to find the projection axes according to the margin maximization criterion. SVDA is an excellent data dimension reduction method which benefits from the intrinsic merits of SVM such as generalization abilities and kernel tricks for nonlinear classification. The proposed algorithm is experimented on the Japanese female facial expression (JAFFE) database. Extensive experiments are carried out to compare with other common methods such as PCA and LDA. The experiment results show that the combination of LBP+SVDA provides better performance than that of those traditional algorithms and prove the effectiveness of the proposed algorithm.
  Image and Signal Processing, 2009. CISP '09. 2nd International Congress on; 11/2009
• Conference Proceeding: A new approach of facial expression recognition based on Contourlet Transform

  Lin-Bo Cai, Zi-Lu Ying
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  ABSTRACT: Contourlet transform provides a flexible multiresolution, local and directional image expansion and a sparse representation for two dimensional piecewise smooth signal resembling images. It overcomes the weakness of wavelet transform in dealing with high dimensional signals. In this paper, the theory of contourlet transform is introduced and a new approach of facial expression recognition based on contourlet transform is proposed. Locally linear embedding is then applied for feature dimensionality reduction, and vector support machine is used to classify the seven expressions (anger, disgust, fear, happiness, neutral, sadness and surprise) of JAFFE database. Both wavelet transform and principal component analysis (PCA) algorithm are carried out to compare with the approach proposed. Experimental results show that the proposed approach gets better recognition rate than both wavelet transform and principal component analysis. The facial expression recognition based on contourlet transform is an effective and feasible algorithm.rlet Transform is an effective and feasible algorithm.
  Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on; 08/2009
• Conference Proceeding: Two-Dimensional Heteroscedastic Discriminant Analysis for Facial Gender Classification.

  Jun-Ying Gan, Si-Bin He, Zi-Lu Ying, Lin-Bo Cai
  Emerging Intelligent Computing Technology and Applications, 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, South Korea, September 16-19, 2009. Proceedings; 01/2009