Abstract：Fuzzy integral theory can be effectively used to deal with the uncertainties of the classification decisions. However, the classification capability of each classifier for recognition results and the supportability of each classifier for the object recognition are not taken into account in the current methods of fuzzy density determination, which results in the loss of the important information for fusion recognition. To overcome this disadvantage, a fusion recognition algorithm based on fuzzy density determination with classification capability and supportability for each classifier is presented. In this algorithm, the fuzzy densities for the classifier fusion are adaptively determined by classification capability of each classifier for recognition results and supportability of each classifier for the object recognition. Thus, the multi-classifiers fusion recognition can be effectively realized. The proposed algorithm is used to recognize facial expression in natural interaction situation and Cohn-Kanade facial expression database. The experimental results show that the proposed algorithm effectively raises the accuracy of expression recognition.
詹永照，张娟，毛启容. 基于可分度和支持度的模糊密度赋值融合识别算法[J]. 模式识别与人工智能, 2012, 25(2): 346-351.
ZHAN Yong-Zhao, ZHANG Juan, MAO Qi-Rong. Fusion Recognition Algorithm Based on Fuzzy Density Determination with Classification Capability and Supportability. , 2012, 25(2): 346-351.
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2012, Vol. 25 
