Abstract
Isometric feature mapping(Isomap) algorithm is topologically unstable if the input data are distorted.Therefore,an improved Isomap algorithm was proposed.In the improved algorithm,Image Euclidean Distance(IMED) was embedded into Isomap algorithm.Firstly,the authors transformed images into image Euclidean Distance(ED) space through a linear transformation by introducing metric coefficients and metric matrix;then,Euclidean distance matrix of images in the transformed space was calculated to find the neighborhood graph and geodesic distance matrix;finally,low-dimensional embedding was constructed by MultiDimensional Scaling(MDS) algorithm.Experiments with the improved algorithm and nearest-neighbor classifier were conducted on ORL and Yale face database.The results show that the proposed algorithm outperforms Isomap with average recognition rate by 5.57% and 3.95% respectively,and the proposed algorithm has stronger robustness for face recognition with small changes.
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.
