An improved face recognition technique based on modular PCA approach

By Rajkiran Gottumukkal, Vijayan K. Asari. Pattern Recognition Letters, 2004

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Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition (2011, background): “The low frequency components retain generic facial textures [12], while largely omitting person-specific information.”
Hierarchical Ensemble of Global and Local Classifiers for Face Recognition (2009, result): “The conflicting results of the two classifiers interestingly reflect the above-mentioned different roles of global and local information, which suggests that ideal classifier should be the combination of the two “experts.””
A Comparative Study of Local Matching Approach for Face Recognition (2007, background): “Recently, local matching approaches have shown promising results not only in face recognition [2], [15], [16], [20], [29], [33], [42], [47], [53] but also in other visual recognition tasks [45].”
Automated Inference on Criminality using Face Images (2016, methodology): “Facial feature vector generated by modular PCA [15]; 3.”
Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, unknown): “…projected-combined principal component analysis) [6], 2DPCA (two-dimensional PCA) [34], (2D) 2 PCA (two-directional two-dimensional PCA) [37], SOM [26], LPP [10], SVD-LDA (singular value decomposition-based linear discriminant analysis) Block PCA [9], Block LDA [5] and UP (uniform pursuit) [7].”