Abstract
In two-dimensional local graph embedding discriminant analysis(2DLGEDA), the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring within the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. But in the real world, face images are always affected by variations in illumination conditions and different facial expressions. So, the fuzzy two-dimensional local graph embedding analysis (F2DLGEA) algorithm is proposed, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the distribution local information of original samples. Experimental results on ORL face databases and PolyU palmprint show the effectiveness of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
