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## IEEE TRANSACTIONS ON IMAGE PROCESSING, 2009 1 Misalignment-Robust Face Recognition

### Citations

3782 |
Introduction to Statistical Pattern Recognition
- Fukunaga
- 1990
(Show Context)
Citation Context ...ur proposed general formulation for misalignment-robust face recognition. I. INTRODUCTION Subspace learning techniques for face recognition have experienced a dramatic growth over the past decade [5] =-=[7]-=- [23] [25]. Among them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace [19], LaplacianFaces [8], Marginal... |

2309 | Eigenfaces vs. fisherfaces: Recognition using class specific linear proposed systemion.
- Belhumeur, Hespanha, et al.
- 1997
(Show Context)
Citation Context ...ce recognition have experienced a dramatic growth over the past decade [5] [7] [23] [25]. Among them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) =-=[3]-=-, Random Subspace [18], Unified Subspace [19], LaplacianFaces [8], Marginal Fisher Analysis [21], Kernel LDA [23], Probabilistic LDA [11], and the recently proposed extensions for handling tensor data... |

1379 |
Face recognition using eigenfaces".
- Turk, Pentland
- 1991
(Show Context)
Citation Context ...CTION Subspace learning techniques for face recognition have experienced a dramatic growth over the past decade [5] [7] [23] [25]. Among them, some popular ones are Principal Component Analysis (PCA) =-=[16]-=-, Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace [19], LaplacianFaces [8], Marginal Fisher Analysis [21], Kernel LDA [23], Probabilistic LDA [11], and the recently prop... |

934 | Robust face recognition via sparse representation
- Wright, Yang, et al.
- 2009
(Show Context)
Citation Context ...es for the elements of ε. In these scenarios, the number of pixels with large-value noises is relatively small compared with the total number of pixels, namely, the ε is sparse. As studied in [4] and =-=[20]-=-, a sparse solution can be achieved by minimizing the `1 norm. Thus a natural way to obtain a solution robust to the above factors is to minimize the `1 norm of the error term ε, such that the large e... |

508 | Using discriminant eigenfeatures for image retrieval.
- Swets, Weng
- 1996
(Show Context)
Citation Context ...igned to search for such a matrix. Principal Component Analysis (PCA) [9] seeks projection directions with maximal variances, namely with the best capability to reconstruct the original data. LDA [3] =-=[14]-=- and its variants [26] [22] search for the directions that are most effective for discrimination by minimizing the ratio between the intra-class and inter-class scatters. Locality Preserving Projectio... |

389 | Face recognition using Laplacianfaces,
- Yan, Niyogi, et al.
- 2005
(Show Context)
Citation Context ...ecade [5] [7] [23] [25]. Among them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace [19], LaplacianFaces =-=[8]-=-, Marginal Fisher Analysis [21], Kernel LDA [23], Probabilistic LDA [11], and the recently proposed extensions for handling tensor data [21] [24]. Subspace learning was originally motivated for overco... |

271 | Graph embedding and extensions: A general framework for dimensionality reduction.
- Yan, Xu, et al.
- 2007
(Show Context)
Citation Context ... them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace [19], LaplacianFaces [8], Marginal Fisher Analysis =-=[21]-=-, Kernel LDA [23], Probabilistic LDA [11], and the recently proposed extensions for handling tensor data [21] [24]. Subspace learning was originally motivated for overcoming the curse of dimensionalit... |

260 |
Principal Components Analysis
- Joliffe
- 1986
(Show Context)
Citation Context ..., (1) where the column vectors of the matrix W constitute a subspace for data representation. Subspace learning algorithms are designed to search for such a matrix. Principal Component Analysis (PCA) =-=[9]-=- seeks projection directions with maximal variances, namely with the best capability to reconstruct the original data. LDA [3] [14] and its variants [26] [22] search for the directions that are most e... |

252 | A Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition,"
- Zhao, Chellappa
- 1998
(Show Context)
Citation Context ...ch a matrix. Principal Component Analysis (PCA) [9] seeks projection directions with maximal variances, namely with the best capability to reconstruct the original data. LDA [3] [14] and its variants =-=[26]-=- [22] search for the directions that are most effective for discrimination by minimizing the ratio between the intra-class and inter-class scatters. Locality Preserving Projection (LPP) [8] tries to p... |

211 | Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class
- Martinez
(Show Context)
Citation Context ...e the data more inseparable; and 3) the number of virtual samples is limited compared with the huge amount of possible spatial misalignments. The work IEEE TRANSACTIONS ON IMAGE PROCESSING, 2009 2 in =-=[10]-=- instead used patch-based philosophy for overcoming misalignment issue. In this paper, we provide our solution to the face recognition problem under the scenarios with spatial misalignments and/or ima... |

186 | Kernel Eigenfaces vs. Kernel fisherfaces: face recognition using Kernel methods,” - Yang - 2002 |

139 | KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition,
- Yang, Frangi, et al.
- 2005
(Show Context)
Citation Context ...roposed general formulation for misalignment-robust face recognition. I. INTRODUCTION Subspace learning techniques for face recognition have experienced a dramatic growth over the past decade [5] [7] =-=[23]-=- [25]. Among them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace [19], LaplacianFaces [8], Marginal Fish... |

121 | Probabilistic linear discriminant analysis for inferences about identity,”
- Prince, Elder
- 2007
(Show Context)
Citation Context ...mponent Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace [19], LaplacianFaces [8], Marginal Fisher Analysis [21], Kernel LDA [23], Probabilistic LDA =-=[11]-=-, and the recently proposed extensions for handling tensor data [21] [24]. Subspace learning was originally motivated for overcoming the curse of dimensionality in the learning process and reducing th... |

103 | A unified framework for subspace face recognition”,
- Wang, Tang
- 2004
(Show Context)
Citation Context ...rowth over the past decade [5] [7] [23] [25]. Among them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace =-=[19]-=-, LaplacianFaces [8], Marginal Fisher Analysis [21], Kernel LDA [23], Probabilistic LDA [11], and the recently proposed extensions for handling tensor data [21] [24]. Subspace learning was originally ... |

85 | Local discriminant embedding and its variants,”
- Chen, Chang, et al.
- 2005
(Show Context)
Citation Context ...of our proposed general formulation for misalignment-robust face recognition. I. INTRODUCTION Subspace learning techniques for face recognition have experienced a dramatic growth over the past decade =-=[5]-=- [7] [23] [25]. Among them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace [19], LaplacianFaces [8], Marg... |

52 | Random sampling for subspace face recognition”,
- Wang, Tang
- 2006
(Show Context)
Citation Context ...xperienced a dramatic growth over the past decade [5] [7] [23] [25]. Among them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace =-=[18]-=-, Unified Subspace [19], LaplacianFaces [8], Marginal Fisher Analysis [21], Kernel LDA [23], Probabilistic LDA [11], and the recently proposed extensions for handling tensor data [21] [24]. Subspace l... |

49 | Automatic eye detection and its validation
- Wang, Green, et al.
- 2005
(Show Context)
Citation Context ... locations of the two eyes in the cropped rectangle [21]. For practical systems, however, the positions of the two eyes need be automatically located by a face alignment algorithm [6] or eye detector =-=[17]-=-, so it is inevitable that there may exist localization errors, namely spatial misalignments. Generally, the spatial misalignments include four components, translations in horizontal and vertical dire... |

41 | C.J.: Comparing active shape models with active appearance models.
- Cootes, Edwards, et al.
- 1999
(Show Context)
Citation Context ...m this is to fix the locations of the two eyes in the cropped rectangle [21]. For practical systems, however, the positions of the two eyes need be automatically located by a face alignment algorithm =-=[6]-=- or eye detector [17], so it is inevitable that there may exist localization errors, namely spatial misalignments. Generally, the spatial misalignments include four components, translations in horizon... |

23 | Spectral regression: A unified approach for sparse subspace learning - Cai, He, et al. - 2007 |

11 | Review the Strength of Gabor Features for Face Recognition from the Angle of its Robustness to Misalignment - Shan, Gao, et al. - 2004 |

11 | Null space versus orthogonal linear discriminant analysis.
- Ye, Xiong
- 2006
(Show Context)
Citation Context ...ed general formulation for misalignment-robust face recognition. I. INTRODUCTION Subspace learning techniques for face recognition have experienced a dramatic growth over the past decade [5] [7] [23] =-=[25]-=-. Among them, some popular ones are Principal Component Analysis (PCA) [16], Linear Discriminant Analysis (LDA) [3], Random Subspace [18], Unified Subspace [19], LaplacianFaces [8], Marginal Fisher An... |