Ensemble of multiple classifiers for face recognition based on synergetic
Chunming Xu, Tianping Zhang, Zhengqun Wang · Journal of Yangzhou University · 2006
Ensemble of multiple classifiers for face recognition based on synergetic is proposed in this paper.Firstly different train samples are selected as prototype patterns which make the prototype patterns diversity.In the recognition stage,order parameters are converted to posteriori probability,then voting and ensemble of posteriori probability based on add rule are used respectively to get finally results;and an improved method for ensemble of posteriori probability based on add rule is also proposed in order to enhance the performance of ensemble.In addition,kernel principal component analysis and synergetic pattern recognition are combined in this paper to improve the classification results,namely,kernel principal component analysis is applied and optimal non-linear features are gotten as prototype patterns,meanwhile the effect of image redundance information is eliminated,then synergetic pattern recognition is applied for face classification.To verify the effectiveness of the proposed method,experiment is tested on Yale face database and experiment result shows that the face recognition method proposed is more available than classical synergetic pattern recognition method and kernel principal component analysis.