Hybrid independent component analysis and support vector machine learning scheme for face detection

In this paper we propose a new hybrid unsupervised / supervised learning scheme that integrates Independent Component Analysis (ICA) with the SupportVector 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 obtained a larger margin of separation and fewer support vectors than by training SVM directly on the image data. This indicates better generalization performance, which was verified in our experiments.