A unified framework for subspace face recognition

By Xiaogang Wang, Xiaoou Tang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

390 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ece540024278a62bfe6ea855a09eb0d99455b09d

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In Signal Processing

Citations in the corpus

Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition (2014, methodology): “To date, two major categories of face representation methods have emerged, namely holistic subspace learning-based methods [2], [3], [4], [5] and face image descriptor-based methods [6], [7], [8], [9]; the latter has dominated recent research due to”
Trace Ratio vs. Ratio Trace for Dimensionality Reduction (2007, methodology): “Discussion: Over the past few decades, many algorithms have been proposed for dimensionality reduction [12][13]; even for just the PCA+LDA/MFA paradigm, numerous different procedures have been proposed on how to select the PCA dimension, and there are two parameters in MFA that”
Latent Low-Rank Representation for subspace segmentation and feature extraction (2011, background): “, [2, 3, 5, 7, 11, 17, 18, 19, 21, 22, 27]) owing to its numerous applications, such as motion segmentation [6, 21, 29], face recognition [26, 28] and image segmentation [15, 18].”
Face Recognition via Archetype Hull Ranking (2013, background): “In the face recognition literature, a large number of subspace methods [25][3][19][17][26][27][13][8][28] working on holistic facial features have been proposed.”
Coupled information-theoretic encoding for face photo-sketch recognition (2011, methodology): “We use a simple PCA+LDA classiﬁer 7 [3, 29] to compute the dissimilarity between a photo and a sketch.”
Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “APPEARANCE-BASED methods have been widely used in face recognition, and a large number of such algorithms have been proposed in recent years [2], [3], [4], [5], [9], [14], [15], [16], [23], [24], [25], [ 41], [46], [49], [51], [53], [55].”
EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking (2007, methodology): “These methods can be implemented by supervised learning method, including linear discriminant analysis [ 23 , 24] or support vector machine (SVM), or simple nearest-neighbor.”
Similarity Metric Learning for Face Recognition (2013, methodology): “To reduce the effect of large intra-personal variations, we follow the idea in [9, 13, 21] by further mapping d-dimensional Eigenfaces to the intra-personal subspace.”
Deep Learning Identity-Preserving Face Space (2013, methodology): “In the future work, we will extend the framework to deal with robust face recognition in other difficult conditions such as expression change and face sketch recognition [25, 30], and will combine FIP features with more classic face recognition approaches to further improve the performance [28, 29, ”
A General Exponential Framework for Dimensionality Reduction (2014, methodology): “Wang et al. [5] modeled face difference with three components and used them to unify PCA, LDA and Bayesian into a general framework.”
Hybrid Deep Learning for Face Verification (2013, background): “Face recognition has been extensively studied in recent decades [29, 28, 30, 1, 16, 5, 33, 12, 6, 3, 7, 25, 34].”
Deeply learned face representations are sparse, selective, and robust (2014, background): “Face recognition achieved great progress thanks to extensive research effort devoted to this area [31, 33, 6, 24, 37, 27, 25, 23, 38].”
Deep Learning Face Representation by Joint Identification-Verification (2014, methodology): “It can be traced back to early subspace face recognition methods such as LDA [1], Bayesian face [17], and unified subspace [23, 24].”
Eigenfeature Regularization and Extraction in Face Recognition (2008, background): “AnotheropenquestionofUFSishowtochoosethenumberof principaldimensionsforthefirsttwostagesofsubspace decompositionsbeforeselectingthefinalnumberoffeatures atthethirdstage.”
Probabilistic Linear Discriminant Analysis for Inferences About Identity (2007, neutral): “Bayesian [13] 1 11.5 Unified Subspace (US)[ 20 ] 1 6.8”
Recover Canonical-View Faces in the Wild with Deep Neural Networks (2014, background): “In the 白rst category , global features such as Eigen faces [33] , Fisher faces [6] , and their extensions [37] can cover global variations due to small pose and simple illumination chang口， but do not work well under large poses and complex illumination conditions.”
Face recognition with learning-based descriptor (2010, background): “Many recent researches also apply learning approaches in face recognition, such as subspace learning [25, 26], metric learning [9], high-level trait learning [13], discriminant model learning [20, 30, 31], but few of these works focus on the issue of local feature encoding [14, 24] and the study of ”
Bayesian Face Revisited: A Joint Formulation (2012, methodology): “In the first experiment, we compare conventional Bayesian face, unified subspace , Wang and Tang's unified subspace work [3], and our joint Bayesian.”
A Practical Transfer Learning Algorithm for Face Verification (2013, positive): “During the testing phase, unlike previous Bayesian face recognition algorithms which discriminate based on the difference between a pair of faces [19, 27], the Joint Bayesian classifier is based upon the full joint distribution of face image pairs leading to a considerable performance boost.”