Dual-space linear discriminant analysis for face recognition

By Xiaogang Wang, Xiaoou Tang. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004

447 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9a828ab27cef4f307fcf747389c15264be69de4c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Dacheng Tao
2. Xiaogang Wang
3. Ping Luo
4. Xiaoou Tang
5. Yi Yang
6. Luc Van Gool
7. Wanli Ouyang
8. Shuicheng Yan

Citations in the corpus

• Discriminant analysis with tensor representation (2005, methodology): “We did not further evaluate the other methods because those methods, such as random subspace [13], dual-space LDA [14] and generalized Singular Value Decomposition [18], can also be applied on DATER with higher order tensors.”
• Graph embedding: a general framework for dimensionality reduction (2005, methodology): “Among them, Null subspace [17] algorithm is the most popular one and it effectively applied the null subspace of the intra-class scatter matrix.”
• Trace Ratio vs. Ratio Trace for Dimensionality Reduction (2007, background): “Many algorithms [1][8][10][11] with various motivations have been proposed to find such a W .”
• General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (2007, methodology): “Aiming at utilizing all discriminative information provided by Sb and Sw, Wang and Tang [42] developed the dual-space method, but they require a threshold, which is difficult to determine in applications.”
• 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.”
• Modeling object appearance using Context-Conditioned Component Analysis (2015, methodology): “The Dual-Space LDA approach [29] combined both of these subspace directions.”
• 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].”
• Eigenfeature Regularization and Extraction in Face Recognition (2008, methodology): “However, applying PCA for dimensionality reduction may result in the loss of discriminative information [20], [21], [22].”
• Face Photo-Sketch Synthesis and Recognition (2009, methodology): “In this paper, we will evaluate the performance of several appearancebased face recognition approaches, including PCA [25], Bayesianface (Bayes) [26], Fisherface [27], null-space LDA [28], dual-space LDA [29], and Random Sampling LDA (RS- LDA) [30], [31].”
• Heterogeneous Face Recognition Using Kernel Prototype Similarities (2013, unknown): “In our implementation, s ¼ 16.”
• A Survey of Face Recognition Techniques (2009, methodology): “Examples of recent LDA-based algorithms include Direct LDA [100, 101], Direct-weighted LDA [102], Nullspace LDA [103, 104], Dual-space LDA [105], Pair-wise LDA [106], Regularized Discriminant Analysis [107], Generalized Singular Value Decomposition [108, 109], Direct Fractional-Step LDA [110],…”