Face Recognition by Combining Multiple Classifiers
Cui Guangliang · 2004
This paper proposes a face recognition method by combining multiple classifiers for information fusion. The similarity between pairs of faces can be modeled as two classes,intra-pattern and inter-pattern. Firstly this idea is used to construct weak learners in local area of wavelet domains. Then the boosting algorithm is used to train the strong classifiers. The final decision of matching is given by weighted combination of multiple weak classifiers. The experimental results show that the system is robust for variation of expression and illumination. The pretty high recognition rate can be achieved even on the new data set in which the individuals are unseen during training process.