FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W183137691> ?p ?o ?g. }

• W183137691 abstract "Robust face recognition is a challenging goal because of the gross similarity in shape and configuration of all human faces accompanied by the large differences between face images of the same person due to variations in lighting conditions, view points, head pose, and facial expressions. These two factors remarkably degrade the distinguishability of human faces because research on face recognition has shown that differences between images of the same face due to nuisance variations are normally greater than those bet ween different faces. This problem is further exacerbated when only one image is available per person for registration and matching. An ideal face recognition system should recognize novel images of a known face and be maximally insensitive to nuisance variations while knowing very little about the image acquisition process. There are two classical methods for face recognition with variation in lighting conditions. One approach is to represent images with features that are insensitive to illumination variations. The other approach is to construct a linear subspace for images of every face under different illumination. Although both of these techniques have been successfully applied to some extent in face recognition, it is hard to extend them for recognition with various facial expressions. Three main approaches are used for expression invariant face recognition: the first approach is to morph images to be the same shape as those used for training: the second is to apply optical flow for comparing images: and the third approach is to assign different weights to local regions that are less sensitive to expression variation. Note that similar approaches cannot be applied to solve the problem of face recognition under variable lighting conditions. An even more difficult task is to recognize face images with both illumination and expression variations due to the fact that features insensitive to illumination changes are often highly sensitive to expression variation. The problem is magnified in applications when only one sample image per face (class) is available. In this paper, we develop an algorithm - Discriminative Principal Component Analysis (DPC A) that can simultaneously deal with variations in face images due to illumination and facial expressions using only a single image per person (class). This approach takes advantages from both Principal Component Analysis (PC A) and Fisher Linear Discriminant (FLD) methods. We first apply PCA to construct a subspace for image representation. We then warp the subspace by whitening and eigen-filtering according to the within-class covariance and between-class covariance of samples to improve the class separability. The proposed technique performs well under changes in lighting conditions because features extracted by PCA are similarly altered for all classes……………" @default.
• W183137691 created "2016-06-24" @default.
• W183137691 creator A5089800102 @default.
• W183137691 date "2005-01-01" @default.
• W183137691 modified "2023-09-26" @default.
• W183137691 title "Robust discriminative principal component analysis for face recognition" @default.
• W183137691 hasPublicationYear "2005" @default.
• W183137691 type Work @default.
• W183137691 sameAs 183137691 @default.
• W183137691 citedByCount "0" @default.
• W183137691 crossrefType "journal-article" @default.
• W183137691 hasAuthorship W183137691A5089800102 @default.
• W183137691 hasConcept C144024400 @default.
• W183137691 hasConcept C153180895 @default.
• W183137691 hasConcept C154945302 @default.
• W183137691 hasConcept C190470478 @default.
• W183137691 hasConcept C195704467 @default.
• W183137691 hasConcept C27438332 @default.
• W183137691 hasConcept C2779304628 @default.
• W183137691 hasConcept C31510193 @default.
• W183137691 hasConcept C31972630 @default.
• W183137691 hasConcept C32834561 @default.
• W183137691 hasConcept C33923547 @default.
• W183137691 hasConcept C36289849 @default.
• W183137691 hasConcept C37914503 @default.
• W183137691 hasConcept C41008148 @default.
• W183137691 hasConcept C4641261 @default.
• W183137691 hasConcept C54654163 @default.
• W183137691 hasConcept C88799230 @default.
• W183137691 hasConcept C97931131 @default.
• W183137691 hasConceptScore W183137691C144024400 @default.
• W183137691 hasConceptScore W183137691C153180895 @default.
• W183137691 hasConceptScore W183137691C154945302 @default.
• W183137691 hasConceptScore W183137691C190470478 @default.
• W183137691 hasConceptScore W183137691C195704467 @default.
• W183137691 hasConceptScore W183137691C27438332 @default.
• W183137691 hasConceptScore W183137691C2779304628 @default.
• W183137691 hasConceptScore W183137691C31510193 @default.
• W183137691 hasConceptScore W183137691C31972630 @default.
• W183137691 hasConceptScore W183137691C32834561 @default.
• W183137691 hasConceptScore W183137691C33923547 @default.
• W183137691 hasConceptScore W183137691C36289849 @default.
• W183137691 hasConceptScore W183137691C37914503 @default.
• W183137691 hasConceptScore W183137691C41008148 @default.
• W183137691 hasConceptScore W183137691C4641261 @default.
• W183137691 hasConceptScore W183137691C54654163 @default.
• W183137691 hasConceptScore W183137691C88799230 @default.
• W183137691 hasConceptScore W183137691C97931131 @default.
• W183137691 hasLocation W1831376911 @default.
• W183137691 hasOpenAccess W183137691 @default.
• W183137691 hasPrimaryLocation W1831376911 @default.
• W183137691 hasRelatedWork W1491741707 @default.
• W183137691 hasRelatedWork W1586081892 @default.
• W183137691 hasRelatedWork W1589407110 @default.
• W183137691 hasRelatedWork W1971078367 @default.
• W183137691 hasRelatedWork W2046730322 @default.
• W183137691 hasRelatedWork W2095990746 @default.
• W183137691 hasRelatedWork W2167387056 @default.
• W183137691 hasRelatedWork W2185691007 @default.
• W183137691 hasRelatedWork W2187526366 @default.
• W183137691 hasRelatedWork W2188753808 @default.
• W183137691 hasRelatedWork W2378109874 @default.
• W183137691 hasRelatedWork W2470700663 @default.
• W183137691 hasRelatedWork W2556692901 @default.
• W183137691 hasRelatedWork W2586392958 @default.
• W183137691 hasRelatedWork W2922706506 @default.
• W183137691 hasRelatedWork W2963170034 @default.
• W183137691 hasRelatedWork W2967424080 @default.
• W183137691 hasRelatedWork W51205587 @default.
• W183137691 hasRelatedWork W89940629 @default.
• W183137691 hasRelatedWork W2185423852 @default.
• W183137691 isParatext "false" @default.
• W183137691 isRetracted "false" @default.
• W183137691 magId "183137691" @default.
• W183137691 workType "article" @default.