General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

By Dacheng Tao, Xuelong Li, Xindong Wu, S. Maybank. 2007

1,189 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

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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].”
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, methodology): “Inspired by recent work on tensor-based decompositions, we tested tensor-based representations for LBP and Gabor features using General Tensor Discriminant Analysis (GTDA) [42] as a dimensionality reduction method.”
Cross-Modality Submodular Dictionary Learning for Information Retrieval (2014, neutral): “While classical approaches [ 34 ] , [ 39 ] , [ 41 ] , [ 20 ] , [ 40 ] , which only utilize a unimodal representation, e.g., text, image, audio, etc., are considered somewhat outdated, multimodal systems [ 37 ] , [ 9 ] , [ 44 ] become popular for information retrieval.”
A Comprehensive Survey on Pose-Invariant Face Recognition (2015, methodology): “…Kernel Principle Component Analysis (PCA) [Liu 2004] and Kernel Linear Discriminant Analysis (LDA) [Kim and Kittler 2006; Huang et al. 2007; Tao et al. 2006, 2007, 2009], were employed to learn nonlinear transformation to a high-dimensional feature space where faces of different subjects…”
On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification (2012, neutral): “D Gabor function can be defined as [33] G s,d (x, y) = G κ ( x) = κ δ 2 · e − κ 2 ··x 2 2δ 2 · e i· κ·x − e − δ 2 2 (2) where x = (x, y) is the spatial domain variable and the Several effective shape features have been proposed, e.g., [34] and [35] proposed an effective length–width extraction…”
Gabor-Based Region Covariance Matrices for Face Recognition (2008, background): “The number of oscillations under the Gaussian envelope is determined by [14].”
Multi-view hypergraph learning by patch alignment framework (2013, methodology): “It unifies spectral analysis based dimensionality reduction approaches, including LLE/NPE/ONPP, ISOMAP, LE/LPP, LTSA/ LLTSA, HLLE, PCA and LDA.”
A survey of multilinear subspace learning for tensor data (2011, methodology): “In [23], the general tensor discriminant analysis (GTDA) algorithm is proposed.”
A Study on Gait-Based Gender Classification (2009, background): “Currently, many gait analysis and recognition methods are appearance-based [23]–[26].”
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 Comprehensive Survey to Face Hallucination (2013, background): “In addition to above vector- and matrix-based subspace learning, it could be similarly extended to multilinear analysis, i.e. tensor analysis (Tao et al. 2007a,b).”
Simple to Complex Transfer Learning for Action Recognition (2016, background): “A variety of approaches [3]–[23] have been proposed for”
Learning Discriminant Face Descriptor (2014, background): “Recently, many advanced subspace analysis methods [15], [16] that address the problem of small sample size (SSS) are proposed to enhance the discriminative ability of the learned subspace.”
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.”
Computational analysis and learning for a biologically motivated model of boundary detection (2008, methodology): “in [16,54,76,77] were introduced in our system its ability to learn its recurrent connection weights from ground truth data would allow our system to surpass the performance of [16,54]; this is a direction that we intend to pursue in future research.”
A review of biometric technology along with trends and prospects (2014, methodology): “Table 4: Summary of behavioral modalities: Modality Feature Set Recognition Techniques Datasets Voice Spectrum, Glottal pulse features, pitch, energy, duration, rhythm, temporal features, phones, idiolects, semantics, accent, pronunciation, LPCC [204], MFCC [205], VQ [206-208], HMM [209], GMM [210, ”