Face recognition using kernel direct discriminant analysis algorithms

By Juwei Lu, K. Plataniotis, Anastasios N. Venetsanopoulos. IEEE Trans. Neural Networks, 2003

698 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/55f8f2211066905b00fc5dac08db44042a48291b

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

A two-stage linear discriminant analysis via QR-decomposition (2005, background): “The research of J. Ye is also supported by fellowships from Guidant Corporation and from the Department of Computer Science and Engineering at the University of Minnesota.”
Human face recognition based on multidimensional PCA and extreme learning machine (2011, methodology): “A kernel machine-based discriminant analysis method [11] that deals with nonlinear distribution of the face pattern has also been used for improved representation of faces under variations in viewpoint, illumination and facial expression.”
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition (2009, methodology): “2006), kernel machines (Kim et al. 2002; Liu 2004; Lu et al. 2003) and hybrid methods (Yang et al.”
Linear discriminant analysis for the small sample size problem: an overview (2014, neutral): “LDA (CLDA) [8], direct LDA (DLDA) [67], kernel DLDA (KDLDA) [28], parameterized DLDA (PDLDA) [56], improved DLDA (IDLDA) [36], pseudoinverse LDA (PILDA) [59], fast PILDA (FPILDA) [27], improved PILDA (IPILDA) [34], LDA/QR [64], approximate LDA (ALDA) [35], PCA ? LDA [5, 57], regularized LDA (RLDA) [”
A survey of multilinear subspace learning for tensor data (2011, background): “Two-dimensional (2D) data include gray-level images in computer vision and pattern recognition [1–4], multichannel EEG signals in biomedical engineering [5, 6], and gene expression data in bioinformatics [7].”
Recent advances in visual and infrared face recognition - a review (2005, background): “A new kernel direct discriminant analysis algorithm for face recognition has been proposed in [91].”
Face recognition from a single image per person: A survey (2006, methodology): “…during the last decades, including probabilistic-based eigenface [31], linear discriminative analysis (LDA) based subspace algorithms [10] [11] [12] [13], support vector machine (SVM) based method [32], feature line method [33], Evolution pursuit [34], and Laplacianfaces [35], et al. All of…”
Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification (2006, background): “These vectors turn out to be very effective if they satisfy φTi Sbφi > 0 at the same time [3], [6], [38].”
Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" (2007, methodology): “The representative techniques are kernel principal component analysis (KPCA) [15] and kernel Fisher discriminant(KFD)[16],[17].Bothhaveproventobeeffective in many real-world applications [18], [ 19 ], [20].”
KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition (2005, methodology): “Subsequent research saw the development of a series of KFD algorithms (see Baudat and Anouar [6], Roth and Steinhage [7], Mika et al. [8], [9], [10], Yang [11], Lu et al. [12], Xu et al. [13], Billings and Lee [14], Gestel et al. [15], Cawley and Talbot [16], and Lawrence and Schö lkopf [17]).”
Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal (2008, background): “It can be considered as a kind of generalization to the well-known linear discriminant analysis (LDA) algorithm and has become a promising feature extraction scheme [19—24] in recent years.”
Heterogeneous Face Recognition Using Kernel Prototype Similarities (2013, methodology): “Kernel PCA [19] and kernel LDA [20], [21] approaches to face recognition have used a similar approach, where a face is represented as the kernel similarity to a collection of prototype images in a high dimensional space.”
Face Recognition Systems: A Survey (2020, methodology): “CNN, convolutional neural network.”
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics (2007, neutral): “Ç”