摘要
模糊积分理论可有效处理分类决策不确定性问题.当前模糊密度的确定方法未考虑各个分类器识别结果的可区分程度及各分类器对识别结果的支持程度,会丢失融合识别的相关信息.文中提出基于可分度和支持度的自适应模糊密度赋值融合识别算法.该算法根据各分类器对待识别样本的识别结果的可区分程度和支持程度对分类器的融合模糊密度进行自适应赋值,从而有效实现多分类器融合识别.将该算法应用于自然交互环境下的人脸表情识别和Cohn-Kanade表情识别.实验结果表明,该算法能有效提高总体表情识别率.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2012年第2期346-351,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金资助项目(No.61003183
61170126)
关键词
融合识别
模糊密度
可分度
支持度
人脸表情识别
Fusion Recognition, Fuzzy Density, Classification Capability, Supportability, FacialExpression Recognition
