Title :
Modular approach on kernel principal component analysis for enhanced face recognition
Author :
Parvathi, V.S. ; Satheesh, S. ; Sankaran, Praveen
Author_Institution :
Dept. of Electron. & Commun., Coll. of Eng., Trivandrum, India
Abstract :
A novel face recognition approach, modular kernel principal component analysis (MKPCA), combining the idea of modularity in a kernel method is proposed in this paper. In this technique, face images are divided into sub images (modular approach) and features are extracted from a high dimensional space formed using a Gaussian kernel. This method combines advantages of both modular PCA - more local features and kernel PCA - nonlinear modelling of data. Simulation results on standard databases show that the proposed MKPCA method of face recognition out performs PCA, modular PCA and kernel PCA in recognition rates.
Keywords :
Gaussian processes; face recognition; feature extraction; image classification; principal component analysis; Gaussian kernel; MKPCA; face image classification; face recognition; feature extraction; modular kernel principal component analysis; nonlinear modelling; Databases; Face; Face recognition; Kernel; Principal component analysis; Training; Vectors; face recognition; kernel PCA; modular PCA;
Conference_Titel :
India Conference (INDICON), 2012 Annual IEEE
Conference_Location :
Kochi
Print_ISBN :
978-1-4673-2270-6
DOI :
10.1109/INDCON.2012.6420742