Face recognition by support vector machines

By Guo-qing Guo, S. Li, Kap Luk Chan. 2000

616 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Face recognition using kernel principal component analysis (2002, methodology): “The proposed method produced better results and a significant reduction in the error rate (16.7%) compared with the performances of the best existing system-linear SVMs [ 6 ].”
Recent advances in visual and infrared face recognition - a review (2005, methodology): “SVM has applied to face recognition [99–102] and gender classification [103].”
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression (2008, methodology): “This has been demonstrated by the author previously for learning in the small sample case, such as face recognition [17], [ 18 ], image retrieval [15], audio classification and retrieval [16], and face expression recognition [12].”
Face Recognition: A Literature Review (2008, methodology): “Reference [67] used the SVMs with a binary tree recognition strategy to tackle the face recognition problem.”
Applications of Support Vector Machines for Pattern Recognition: A Survey (2002, methodology): “(Recognition Rate 97%) - bottom-up tree multi-class method - input feature for SVM was extracted by PCA [9, 48 ]”
Face recognition: component-based versus global approaches (2003, methodology): “A bottom-up tree, similar to the elimination tree used in tennis tournaments, was originally proposed in [23] for recognition of 3-D objects and was applied to face recognition in [24].”
A Survey of Face Recognition Techniques (2009, result): “SVM has been employed for face recognition by several other researchers and has been shown to yield good results [169, 171-175].”
Illumination normalization for robust face recognition against varying lighting conditions (2003, background): “However, face recognition remains a difficult, unsolved problem in general.”
A survey on visual surveillance of object motion and behaviors (2004, background): “In recent years, great progress in face recognition [46]–[50] has been achieved.”
Component-Based Face Recognition with 3D Morphable Models (2003, methodology): “Some implementations of the pairwise approach include [14], [7], and [13].”
Total variation models for variable lighting face recognition (2006, methodology): “Various methods have been proposed for face recognition, including Eigenface [42], Fisherface [5], Probablistic and Bayesian Matching [25], subspace LDA [49], Active Shape Model and Active Appearance Model [23], LFA [27], EBGM [45], and SVM [17].”
Face recognition with support vector machines: global versus component-based approach (2001, methodology): “A bottom-up tree similar to the elimination tree used in tennis tournaments was originally proposed in [16] for recognition of 3-D objects and was applied to face recognition in [7].”