Loading...

Tensor-based face representation and recognition using multi-linear subspace analysis

Mohseni, H ; Sharif University of Technology

525 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/CSICC.2009.5349654
  3. Abstract:
  4. Discriminative subspace analysis is a popular approach for a variety of applications. There is a growing interest in subspace learning techniques for face recognition. Principal component analysis (PCA) and eigenfaces are two important subspace analysis methods have been widely applied in a variety of areas. However, the excessive dimension of data space often causes the curse of dimensionality dilemma, expensive computational cost, and sometimes the singularity problem. In this paper, a new supervised discriminative subspace analysis is presented by encoding face image as a high order general tensor. As face space can be considered as a nonlinear submanifold embedded in the tensor space, a decomposition method called Tucker tensor is used which can effectively decomposes this sparse space. The performance of the proposed method is compared with that of eigenface, Fisherface, tensor LPP, and ORO4x2 on ORL and Weizermann databases. Conducted experimental results show the superiority of the proposed method. ©2009 IEEE
  5. Keywords:
  6. Face recognition ; Multi-linear discriminant analysis ; Tensor ; Computational costs ; Curse of dimensionality ; Data space ; Decomposition methods ; Eigenfaces ; Face images ; Face representations ; Face space ; Fisherface ; High order ; Linear discriminant analysis ; Linear subspace ; Singularity problems ; Subspace analysis ; Subspace learning ; Tensor spaces ; Computational methods ; Discriminant analysis ; Image retrieval ; Learning algorithms ; Principal component analysis ; Tensors ; Face recognition
  7. Source: 2009 14th International CSI Computer Conference, CSICC 2009, 20 October 2009 through 21 October 2009, Tehran ; 2009 , Pages 658-663 ; 9781424442621 (ISBN)
  8. URL: http://ieeexplore.ieee.org/document/5349654/?reload=true