Aug 14, 2012 · Inspired by nonnegative matrix factorization (NMF), which is based on localized features, we propose a novel algorithm for face recognition ...
2D-PCA has been successfully applied to face recognition and has higher accuracy than the conventional PCA. It was also successfully applied to other problems ...
2DNPCA is a matrix-based algorithm to preserve the local structure of facial images and has the nonnegative constraint to learn localized components. Therefore, ...
In this paper, we propose a novel approach to extract the facial features called two dimension nonnegative partial least squares (2DNPLS). Our approach can grab ...
Abstract- In this paper, Face Recognition is performed using 2D Principal Component Analysis based dimension reduction technique. 2DPCA is the traditional ...
A method for human face recognition is proposed, namely, non-negative 2-dimensional principal component analysis (N2DPCA). N2DPCA integrates the merits of 2DPCA ...
Jul 4, 2020 · The purpose of this study, to analyze the Facial Image Recognition based on the method of Two-Dimensional Principal Component Analysis (2DPCA) ...
Sep 10, 2021 · The 2DPCA-based approaches for face recognition are also improved by weighting each principle component a scatter measure, which increases ...
The basic idea is to compress the face image from a high dimensional space to on with lower dimensions through a linear or non-linear transformation. These ...
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What is the principal component analysis for face recognition?
What is principal component analysis image recognition?
The proposed approach first performs two-dimensional principal component analysis process to project the faces onto the feature pace and then performs kernel ...