Face recognition by independent component analysis

By M. Bartlett, Javier R. Movellan, Terrence J. Sejnowski. IEEE Trans. Neural Networks, 2002

2,239 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3d2225253964183b14abcd168d024efe00422dd3

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Citations in the corpus

2D and 3D face recognition: A survey (2007, background): “Bartlett Marian et al. (2002) ICA FERET 60 · 50 425–421 Yes 89 Yes No No No No”
Two-dimensional PCA: a new approach to appearance-based face representation and recognition (2004, methodology): “Bartlett et al. [11] and Draper et al. [12] proposed using ICA for face representation and found that it was better than PCA when cosines were used as the similarity measure (however, their performance was not significantly different if the Euclidean distance is used).”
Facial expression recognition based on Local Binary Patterns: A comprehensive study (2009, methodology): “Holistic spatial analysis including Principal Component Analysis (PCA) [38], Linear Discriminant Analysis (LDA) [39], Independent Component Analysis (ICA) [40] and Gabor wavelet analysis [7] have been applied to either the whole-face or speciﬁc face regions to extract the facial appearance changes.”
An automated palmprint recognition system (2005, background): “[25] and set the parameters for InfoMax as below:”
Past, Present, and Future of Face Recognition: A Review (2020, background): “[53] (2002) noted that PCA’s baseline images are only dependent on pair-wise relationships between pixels in the image dataset.”
Independent component analysis of Gabor features for face recognition (2003, background): “Popularstatisticaltechniquesplay a very importantrole in developingfacerecognitionmeth- ods[42], [4], [35], [15], [1], [10], [20].”
Learning multiple layers of representation. (2007, background): “These restrictions ensure that the posterior distribution collapses to a single point because there is only one setting of the hidden variables that can generate each visible vector exactly [9–11].”
Face recognition across pose: A review (2009, background): “Though many face recognition approaches, for example [4,7,27,35,43,53,75], reported satisfactory performances, their successes are limited to the conditions of controlled environment, which are unrealistic in many real applications.”
A Comparative Study of Local Matching Approach for Face Recognition (2007, methodology): “The principle of holistic methods is to construct a subspace using principal component analysis (PCA) [21], [44], [46], linear discriminant analysis (LDA) [4], [13], [26], [41], [48], or independent component analysis (ICA) [3].”
Robust Facial Expression Recognition Based on Local Directional Pattern (2010, methodology): “More recently, independent component analysis (ICA) [19], [20], enhanced ICA [3], and Gabor wavelet [21] have been utilized to extract facial feature either from whole-face or specific face regions for modeling facial changes.”
Recent advances in visual and infrared face recognition - a review (2005, methodology): “Motivated by the fact that much of the important information may be contained in the high-order relationship, face recognition based on independent component analysis (ICA) was proposed as a generalization that is sensitive to higher-order statistics, not second-order relationships [92,93].”
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.”
A Feature Fusion Based Forecasting Model for Financial Time Series (2014, methodology): “Bartlett et al [43] have proposed three methods to tackle this problem: (1) Method based on an amplitude of weight vector; (2) PCA-ICA method; and (3) Scaling factor method based on cluster analysis.”
Independent comparative study of PCA, ICA, and LDA on the FERET data set (2005, methodology): “For example, Liu and Wechsler (1999) and Bartlett et al. (2002) claim that ICA outperforms PCA, while Baek et al. (2002) claim that PCA is better.”
2D-LDA: A statistical linear discriminant analysis for image matrix (2005, methodology): “Bartlett et al. (2002) proposed using ICA for face representation, which is sensitive to the high-order”
Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification (2006, methodology): “Finally, statistical independence in the low-dimensional feature space can be also used as a criterion in order to find the linear projections, e.g., Independent Component Analysis (ICA) [4], [20].”