• Corpus ID: 18191554

Chapter 3 Face Subspace Learning

  title={Chapter 3 Face Subspace Learning},
  author={Wei Bian and Dacheng Tao},
The last few decades have witnessed a great success of subspace learning for face recognition. From principal component analysis (PCA) [43] and Fisher’s linear discriminant analysis [1], a dozen of dimension reduction algorithms have been developed to select effective subspaces for the representation and discrimination of face images [17, 21, 45, 46, 51]. It has demonstrated that human faces, although usually represented by thousands of pixels encoded in high-dimensional arrays, they are… 


Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Face recognition using Laplacianfaces
Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
Random Sampling for Subspace Face Recognition
An ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters is developed, and a robust random sampling face recognition system integrating shape, texture, and Gabor responses is constructed.
Robust Face Recognition via Sparse Representation
This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Face Recognition in Subspaces
This chapter describes in roughly chronologic order techniques that identify, parameterize, and analyze linear and nonlinear subspaces, from the original Eigenfaces technique to the recently introduced Bayesian method for probabilistic similarity analysis.
A unified framework for subspace face recognition
This paper first model face difference with three components: intrinsic difference, transformation difference, and noise, and builds a unified framework by using this face difference model and a detailed subspace analysis on the three components.
Subspace analysis using random mixture models
  • Xiaogang Wang, Xiaoou Tang
  • Computer Science
    2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
This paper develops a random mixture model to improve Bayes and LDA subspace analysis by clustering the intrapersonal difference and constructing multiple low dimensional subspaces by randomly sampling on the high dimensional feature vector and randomly selecting the parameters for sub space analysis.
Geometric Mean for Subspace Selection
Preliminary experimental results show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.
Using Graph Model for Face Analysis
It is shown that LPP provides a more general framework for subspace learning and a natural solution to the small sample issue in LDA.
Patch Alignment for Dimensionality Reduction
A new dimensionality reduction algorithm is developed, termed discrim inative locality alignment (DLA), by imposing discriminative information in the part optimization stage, and thorough empirical studies demonstrate the effectiveness of DLA compared with representative dimensionality Reduction algorithms.