Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition

By Jen-Tzung Chien, Chia-Chen Wu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

499 citations recorded by the source corpus.

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

Face Recognition with Convolutional Neural Networks and subspace learning (2017, background): “A practical face recognition system involves the stages of face detection, face alignment, feature extraction, and classification, where feature extraction is one of the crucial issues [1].”
Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes (2014, methodology): “Actually, the previous works, like the nearest feature line [2], the nearest feature plane and the nearest feature space methods [3] are all variants of LR based methods.”
Human face recognition based on multidimensional PCA and extreme learning machine (2011, methodology): “Common wavelet based face recognition architectures include wavelet based PCA [16], wavelet based LDA [17], wavelet based kernel association memory (kAM) [18] and wavelet based modular weighted PCA [19].”
Linear Regression for Face Recognition (2010, methodology): “In [11], a two-step approach has been adopted, fusing the concept of wavelet decomposition and discriminant analysis to design a sophisticated feature extraction stage.”
Learning Discriminant Face Descriptor (2014, methodology): “Among various classification methods, the nearest neighbors (NN) classifier and its variants, i.e., nearest feature line [1] or nearest subspace [2], are the most popular methods in face recognition.”