Paper Info
Title
Hybrid independent component analysis and support vector machine learning scheme for face detection
Abstract
We propose a new hybrid unsupervised/supervised learning scheme that integrates independent component analysis (ICA) with the support vector machine (SVM) approach and apply this new learning scheme to the face detection problem. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, SVM classifies the ICA features as a face or non-faces. Our experimental results show that by using ICA features we obtain a larger margin of separation and fewer support vectors than by training SVM directly on the image data. This indicates better generalization performance, which is verified in our experiments
Year
DOI
Venue
2001
10.1109/ICASSP.2001.941211
ICASSP
Keywords
Field
DocType
face detection,generalization performance,learning automata,high-level classification,face recognition,new learning scheme,independent image base,fewer support vector,low-level feature extraction,face detection problem,svm,independent component analysis,hybrid independent component analysis,higher order statistics,feature extraction,image classification,support vector machine,edge detection,hybrid unsupervised supervised learning,edge information,supervised learning scheme,image data,ica feature,generalisation (artificial intelligence),ica,image bases,support vector machines,machine learning,supervised learning,support vector
Facial recognition system,Pattern recognition,Computer science,Edge detection,Support vector machine,Feature extraction,Supervised learning,Artificial intelligence,Independent component analysis,Face detection,Contextual image classification,Machine learning
Conference
Volume
ISSN
ISBN
3
1520-6149
0-7803-7041-4
Citations 
PageRank 
References 
10
0.85
7
Authors
3
Name
Order
Citations
PageRank
Yuan Qi1100.85
David Doermann24313312.70
Daniel Dementhon31327139.94