Enhanced locality sensitive discriminant analysis for image recognition

Enhanced locality sensitive discriminant analysis for image recognition

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An improved manifold learning method, called enhanced locality sensitive discriminant analysis (ELSDA), for image recognition is proposed. Motivated by the fact that statistically uncorrelated and parameter-free are two desirable and promising characteristics for feature extraction, a new difference-based optimisation objective function with uncorrelated constraint for appearance-based image recognition has been designed. Experimental results demonstrate the efficacy of the proposed method.


    1. 1)
      • Face recognition using Laplacianface
    2. 2)
      • Graph embedding and extensions: a general framework for dimensionality reduction
    3. 3)
      • Cai, D., He, X., Zhou, K., Han, J.W., Bao, H.J.: `Locality sensitive discriminant analysis', Proc. Int. Joint Conf. on Artificial Intelligence, 2007, Hyderabad, India, p. 708–713
    4. 4)
      • Face recognition based on the uncorrelated discriminant tranformation
    5. 5)
      • From few to many: illumination cone models for face recognition under variable lighting and pose
    6. 6)
      • On-line palmprint identification
    7. 7)
      • Eigenfaces for recognition
    8. 8)
      • Eigenfaces vs. Fisherface: recognition using class specific linear projection

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