Independent component analysis of Gabor features for face recognition

By Chengjun Liu, Harry Wechsler. IEEE Trans. Neural Networks, 2003

525 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0aac6bdf65264108700f5c27fdecde8a85d384cb

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In Computer Vision and Pattern Recognition

In Media Technology

In Signal Processing

Citations in the corpus

Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification (2006, background): “reader can refer to [1], [3], [4], [5], [6], [7], [8], [9], [10].”
A review on Gabor wavelets for face recognition (2006, methodology): “Both linear and kernel subspace techniques have been adopted in this context, e.g., LDA [35], ICA [ 67 ], Kernel PCA [68], GDA [37], KDDA [69] and Null LDA [70] etc.”
Gabor-based kernel PCA with fractional power polynomial models for face recognition (2004, methodology): “Popular representation methods for face recognition include Principal Component Analysis (PCA) [22], [47], [26], shape and texture (‘shape-free’ image) of faces [9], [4], [49], [24], [27], and Gabor wavelet representation [12], [31], [23], [50], [28].”
Facial expression recognition using radial encoding of local Gabor features and classifier synthesis (2012, methodology): “Among the former are subspace methods such as ‘‘Eigenfaces’’ and ‘‘Fisherfaces’’ [4,5], which consider the complete face as an input, and extract features corresponding to expressions by constructing a subspace using principal component analysis (PCA) [6], independent component analysis (ICA) [7] or”
Face Recognition (2018, unknown): “in [17] presented a 2D-SVD scheme for 2D cases.”
Dominant Local Binary Patterns for Texture Classification (2009, unknown): “[8], [11], [15], [16], [21], [24], [27].”
Facilitating fashion camouflage art (2013, background): “The most popular local features used in face recognition systems are Local Binary Pattern [23] and Gabor Features [24].”
Boosting Local Binary Pattern (LBP)-Based Face Recognition (2004, methodology): “Up to now, many representation approaches have been introduced, including Principal Component Analysis (PCA) [15], Linear Discriminant Analysis (LDA) [3], independent component analysis(ICA) [1, 8 ], and Gabor wavelet features [18].”
Modeling Stylized Character Expressions via Deep Learning (2016, background): “These features can be geometry based such as facial landmarks [16,17], appearance based such as Local Binary Patterns (LBP) [18], Gabor filters [19], Haar features [20], Histogram of Oriented Gradients [21], or motion based such as optical flow [22] and Volume LBP [23].”
Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors (2007, methodology): “To investigate the performance of nonlinear feature extraction process using kernel PCA and kernel ICA, we calculated the average of Euclidean distance between points in class of feature space (Bartlett, Movellan, & Sejnowski, 2002; Liu & Wechsler, 2003).”
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition (2009, methodology): “1997), Subclass Discriminant Analysis (SDA) (Manli and Martinez 2006), Independent Component Analysis (ICA) (Bartlett et al. 2002; Liu and Wechsler 2003), Locality Preserving Projections (LPP) (He et al.”