IEEE Transactions on Pattern Analysis and Machine Intelligence
Face detection using discriminating feature analysis and Support Vector Machine
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICA-based neighborhood preserving analysis for face recognition
Computer Vision and Image Understanding
Techniques for efficient and effective transformed image identification
Journal of Visual Communication and Image Representation
Independent components extraction from image matrix
Pattern Recognition Letters
New colour SIFT descriptors for image classification with applications to biometrics
International Journal of Biometrics
A bi-objective optimization model for interactive face retrieval
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Content-based facial image retrieval using constrained independent component analysis
Information Sciences: an International Journal
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Recent advances in subspace analysis for face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Image classification by multimodal subspace learning
Pattern Recognition Letters
The small sample size problem of ICA: A comparative study and analysis
Pattern Recognition
Discriminant analysis and similarity measure
Pattern Recognition
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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.