Two-dimensional PCA: a new approach to appearance-based face representation and recognition

By Jian Yang, David Dapeng Zhang, Alejandro F Frangi, Jing-yu Yang. 2004

3,748 citations recorded by the source corpus.

How researchers see this paper

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

1. Deng Cai
2. Dacheng Tao
3. Lei Zhang
4. Xuelong Li
5. Xiaofei He
6. Jian Yang
7. Jiwen Lu
8. Mehrtash Harandi

Citations in the corpus

• Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “With advances in mathematical tools and algorithms dealing with tensor input, many multilinear discriminant techniques as well as tensor regression have been proposed for image and video classification problems such as [18], [19], [20], [21], [22], [23], [24], [25], [26], [27].”
• A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition (2011, methodology): “The principal component analysis (PCA) [1]–[3] and the linear discriminant analysis (LDA) [4], [5] are two typical examples of linear transform methods.”
• Factor models for matrix-valued high-dimensional time series (2016, methodology): “Remark 5: Similar models as model (1) have been proposed and studied when conducting principal component analysis on matrix-valued data (e.g. Paatero and Tapper, 1994; Yang et al., 2004; Ye, 2005; Ding and Ye, 2005; Zhang and Zhou, 2005; Crainiceanu et al., 2011; Wang et al., 2016).”
• Tensor Subspace Analysis (2005, methodology): “Recently, multilinear algebr a, the algebra of higher-order tensors, was applied for analyzing the multifactor structu re of image ensembles [9], [11], [12].”
• Face Recognition with Local Binary Patterns (2012, background): “D PCA [8] proffer consistent results in precise environment but have limitations when variation in several factors occur.”
• (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition (2005, methodology): “reported in [ 7 ] that the recognition accuracy on several face databases was higher using 2DPCA”
• Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, unknown): “…principal component analysis) [32], E(PC) 2 A (enhanced projected-combined principal component analysis) [6], 2DPCA (two-dimensional PCA) [34], (2D) 2 PCA (two-directional two-dimensional PCA) [37], SOM [26], LPP [10], SVD-LDA (singular value decomposition-based linear discriminant…”
• Visual saliency detection by spatially weighted dissimilarity (2011, methodology): “To spend less time on determining the corresponding eigenvectors and evaluate the covariance matrix more accurately, we will try to use the two-dimensional PCA introduced in Yang et al. [25], which does not transform the image matrix into a vector.”
• Discriminant Locally Linear Embedding With High-Order Tensor Data (2008, background): “Other works [6], [12], [14], [27], [31]–[33], [35], [36], [48], [50], [52]–[54] employ an image-as-matrix representation, in which a 2-D gray-level image is directly represented as a secondorder data tensor (matrix), or is filtered by Gabor functions [8], [39] to obtain a set of images that comprise”
• A survey of multilinear subspace learning for tensor data (2011, methodology): “Better dimensionality reduction results than [56] are obtained according to [57].”
• Multilinear Discriminant Analysis for Face Recognition (2007, methodology): “1) Connections to LDA and 2-D LDA: LDA and 2-D LDA [25] both optimize the so-called Fisher criterion”
• A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification (2019, methodology): “These eigenvectors will be used later to project 𝑇 𝑁×512 into a new feature compact matrix, 𝑌 𝒩×K by the following equation [22]: The steps of PCA algorithm to calculate the 𝑉 512×𝒦 are given in the following: Suppose that ℒ is number of brain tumor classes in training dataset ( 𝑇 ) of 𝒩…”
• Linear discriminant analysis: A detailed tutorial (2017, unknown): “• Yale dataset 14 is another face images dataset which contains 165 grey scale images in GIF format of 15 individuals [78].”
• Subspace Learning Based on Tensor Analysis (2005, methodology): “Recently, multilinear algebra, the algebra of higher-order tensors, was applied for analyzing the multifactor structure of image ensembles [10, 11, 12].”
• Linear vs. quadratic discriminant analysis classifier: a tutorial (2016, background): “Yale(12) face dataset images contain 165 grey scale images in GIF format of 15 individuals (Yang et al., 2004).”