Enhanced independent component analysis and its application to content based face image retrieval.
This paper describes an enhanced independent component analysis (EICA) method and its application to content based face image retrieval. EICA, whose enhanced retrieval performance is achieved by means of generalization analysis, operates in a reduced principal component analysis (PCA) space. The dimensionality of the PCA space is determined by balancing two competing criteria: the representation criterion for adequate data representation and the magnitude criterion for enhanced retrieval performance. The feasibility of the new EICA method has been successfully tested for content-based face image retrieval using 1,107 frontal face images from the FERET database. The images are acquired from 369 subjects under variable illumination, facial expression, and time (duplicated images). Experimental results show that the independent component analysis (ICA) method has poor generalization performance while the EICA method has enhanced generalization performance; the EICA method has better performance than the popular face recognition methods, such as the Eigenfaces method and the Fisherfaces method.
Liu
Chengjun
C
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. liu@cs.njit.edu
D000465:Algorithms; D005145:Face / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated; D010781:Photography / Q000379:methods*; D025341:Principal Component Analysis