Cascade of Fusion for Adaptive Classifier Combination Using Context-Awareness

摘要

This paper proposes a novel adaptive classifier combination scheme based on the cascade of classifier selection and fusion, called adaptive classifier combination scheme (ACCS). In the proposed scheme, system working environment is learned and the environmental context is identified. GA is used to search most effective classifier systems for each identified environmental context. The group of selected classifiers is combined based on GA model for reliable fusion. The knowledge of individual context and its associated chromosomes representing the optimal classifier combination is stored in the context knowledge base. Once the context knowledge is accumulated the system can react to dynamic environment in real time. The proposed scheme has been tested in area of face recognition using standard FERET database, taking illumination as an environmental context. Experimental result showed that using context awareness in classifier combination provides robustness to varying environmental conditions.