Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods

By Ming-Hsuan Yang. Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, 2002

837 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f0c48ad56bd809ef4d2e166b4c1d9fd238356e00

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Dacheng Tao
2. Lei Zhang
3. Shuicheng Yan
4. Jiwen Lu
5. Weihong Deng
6. Yi Yang
7. Luc Van Gool
8. Deng Cai

Citations in the corpus

• Maximum Neighborhood Margin Discriminant Projection for Classification (2014, methodology): “Among the DR techniques, principal component analysis (PCA) [2, 3], linear discriminant analysis (LDA) [3, 4], locality preserving projection (LPP) [5, 6], and their kernelized and tensorized variants [7–18] are the most representative and well-known algorithms.”
• Two-dimensional PCA: a new approach to appearance-based face representation and recognition (2004, methodology): “Yang [14] used Kernel PCA for face feature extraction and recognition and showed that the Kernel Eigenfaces method outperforms the classical Eigenfaces method.”
• Linear Regression for Face Recognition (2010, methodology): “We follow two evaluation protocols as proposed in the literature quite often [24], [ 25 ], [26], [27].”
• 2D-LDA: A statistical linear discriminant analysis for image matrix (2005, background): “Recently, Yang (2002) investigated the Kernel PCA for learning low dimensional representations for face recognition and found that the Kernel methods provide better representations and achieve lower error rates for face recognition.”
• Multilinear Discriminant Analysis for Face Recognition (2007, methodology): “Most traditional algorithms, such as the principal component analysis (PCA) [17], [20], [21], [24] and linear discriminant analysis (LDA)”
• Local discriminant embedding and its variants (2005, methodology): “Since the database is small and the images are already cropped, we use the whole database for the leave-one-out test (referred to as AT&T LOO), in accordance with the setups of previous works [27], [29].”
• Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics (2007, unknown): “Ç”
• KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition (2005, methodology): “J. Yang, J.-y. Yang, and Z. Jin are with the Department of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, PR China.”
• Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations (2007, methodology): “The methods based on LDA have been widely advocated in the literature [7], [9], [29], [30], [35], [18].”
• Kernel Methods in Computer Vision (2009, methodology): “kernel fisherfaces: Face recognition using kernel methods, IEEE Conference on Automatic Face and Gesture Recognition 2002 Motivated by the fact that many successful techniques for face recognition rely on linear dimensionality reduction, [Yang, 2002] applies kernelPCA and KDA to face images, thereby”
• Independent component analysis of Gabor features for face recognition (2003, background): “TheIGF methodintroducedin this paperdiffersfrom theonein [28], asthelattermethodrequiresmanualannotation,which is a non-trivial challenge for implementingfastandautomatedfacerecognitionsystems.”
• 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].”
• A nonlinear approach for face sketch synthesis and recognition (2005, background): “KNDA is a nonlinear version of LDA, and it is widely used in pattern recognition [14,15,16].”
• 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.”
• Neighborhood repulsed metric learning for kinship verification (2012, unknown): “Representative examples include face recognition [5], [8], [10], [20], [22], [27], [28], [35], [37], [40], [41], [42], [43], [54], [55], [56], [58], [64], [65], [66], [67], [68], facial expression recognition [11], [18], [70], facial age estimation [19], [21], [24], [25], [31], [39], gender classifi”