Title :
Selecting the Principal Feature Components in the Three-dimensional Parameter Space for Face Recognition
Author :
Junbao, Li ; Shuchuan, Chu ; Jengshyang, Pan
Author_Institution :
Harbin Inst. of Technol., Harbin
Abstract :
This paper presents a novel face recognition method of selecting the principal feature components in the 3D parameter space constructed using the dimensions of three subspaces, i.e., PCA subspace, LDA subspace and LPP subspace, as axes. The global, local and clustering structure information can be used fully to enhance the recognition performance by selecting the principal feature component in 3D parameter space. The feasibility of the proposed method is successfully tested on the ORL and Yale face databases.
Keywords :
face recognition; principal component analysis; 3D parameter space; LDA subspace; LPP subspace; ORL face database; PCA subspace; Yale face database; face recognition; principal feature components; Automatic control; Automatic testing; Electronic equipment testing; Face recognition; Feature extraction; Information management; Instruments; Linear discriminant analysis; Principal component analysis; Space technology; Face recognition; linear discriminant analysis; locality preserving projection; principal component analysis; three-dimensional parameter space;
Conference_Titel :
Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-1136-8
Electronic_ISBN :
978-1-4244-1136-8
DOI :
10.1109/ICEMI.2007.4350440
