Orthogonal Laplacianfaces for Face Recognition

By Deng Cai, Xiaofei He, Jiawei Han, HongJiang Zhang. IEEE Transactions on Image Processing, 2006

793 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4516c90e553e2af25dc5f5e594409fb645d1abd8

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In Computer Vision and Pattern Recognition

Citations in the corpus

Dimensionality reduction on Anchorgraph with an efficient Locality Preserving Projection (2016, neutral): “Table 2 Implementation time (s) for dimensionality reduction in the original feature space Method PCA NPE LPP OLPP AgLPP USPS-All 0.14 10.13 5.68 6.625 3.88þ0.”
Human Age Estimation With Regression on Discriminative Aging Manifold (2008, background): “It can be seen that the LPP+regression and OLPP+regression are still comparable and significantly outperform the PCA+regression and NPP+regression.”
A Comprehensive Survey to Face Hallucination (2013, methodology): “Kumar and Aravind (2008b) proposed a two-step method using orthogonal locality preserving projections (OLPP) (Cai et al. 2006) and kernel ridge regression (KRR).”
Maximum Neighborhood Margin Discriminant Projection for Classification (2014, background): “On the contrary, the unsupervised LPPbased ones do not consider class information [21, 22, 33, 34].”
Teaching Semi-Supervised Classifier via Generalized Distillation (2018, methodology): “Speciﬁcally, the ORL dataset [Cai et al. , 2006] is employed, which contains totally 400 face images belonging to 40 subjects.”
A survey of multilinear subspace learning for tensor data (2011, background): “In future work, more complex multilinear mapping can be investigated, for example, by developing multilinear extensions of graph-embedding algorithms such as Isomap [104], Locally Linear Embedding [105], and Locality Preserving Projections [106,107] under the MSL framework.”
Learning a Spatially Smooth Subspace for Face Recognition (2007, background): “Previous works have demonstrated that the face recognition performance can be improved significantly in lower dimensional linear subspaces [1, 6, 16, 24].”
Age Synthesis and Estimation via Faces: A Survey (2010, methodology): “An effective algorithm, called Orthogonal Locality Preserving Projections (OLPP) [137], is to connect the manifold learning with subspace learning.”
Generalized BackPropagation, Étude De Cas: Orthogonality (2016, background): “Examples include but not limited to dimensionality reduction Turk and Pentland (1991); Hyvarinen (1999); Cai et al. (2006); Cunningham and Ghahra-mani (2015), clustering Niu et al. (2010); Kumar et al. (2011), feature selection Yu and Liu (2004) and dictionary learning Lesage et al. (2005); Vidal…”
Trace optimization and eigenproblems in dimension reduction methods (2011, methodology): “Note in passing that a diﬀerent technique was developed in [10] and named Orthogonal Laplacean faces, which is also sometimes referred to as OLPP.”
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition (2009, background): “2002; Liu and Wechsler 2003), Locality Preserving Projections (LPP) (He et al. 2005; Cai et al. 2006), kernel machines (Kim et al.”
Learning Multiple Tasks with a Sparse Matrix-Normal Penalty (2010, methodology): “Intuitively, free diagonal entries in Σ are useful when features are of different importance, e.g, components extracted as orthogonal Laplacianfaces usually capture decreasing amounts of information [10].”
Bregman Divergence-Based Regularization for Transfer Subspace Learning (2010, background): “Almost all existing subspace learning algorithms [2], [11], [12], [20], [40] have been developed by assuming the training and testing samples are i.i.d. variables.”