Robust error correction with multi-model representation for face recognition

The present invention provides a face recognition method on a computing device, comprising: storing a plurality of training face images, each training face image corresponding to a face class; obtaining one or more face test samples; applying a representation model to represent the face test sample...

Full description

Saved in:
Bibliographic Details
Main Authors Wang Haohong, Iliadis Michael
Format Patent
LanguageEnglish
Published 21.02.2017
Subjects
CALCULATING
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
Online AccessGet full text

Cover

More Information
Summary:The present invention provides a face recognition method on a computing device, comprising: storing a plurality of training face images, each training face image corresponding to a face class; obtaining one or more face test samples; applying a representation model to represent the face test sample as a combination of the training face images and error terms, wherein a coefficient vector is corresponded to the training face images; estimating the coefficient vector and the error terms by solving a constrained optimization problem; computing a residual error for each face class, the residual error for a face class being an error between the face test sample and the face test sample's representation model represented by the training samples in the face class; classifying the face test sample by selecting the face class that yields the minimal residual error; and presenting the face class of the face test sample.
Bibliography:Application Number: US201414587562