Research On The Applications Of Information Fusion In Pattern Recognition Problem

Multisensor information fusion is a hotspot in the field of intelligent information processing. Because observing patterns to be recognized from multiple sensors can well and truly reflect the features of them, eliminate the uncertainty of the information. Pattern recognition methods based on information fusion have already been one of the development trends of pattern recognition field. At present, multisensor information fusion in pattern recognition is mainly applied in feature level and decision level.In this thesis, centering on the theoretical fundamental and application of multisensor information fusion technology, the applications of information fusion in pattern recognition is discussed from the angle of feature level fusion and decision level fusion.First, a new method of feature level fusion pattern recognition is presented. Feature Fusion Coefficients are defined to fuse the features extracted from different view of multiple sensors. By evaluating different feature fusion coefficients to different features, we can get the fusion feature of the pattern to be observed. Thus, feature level fusion pattern recognition can be realized.Second, in the theoretical framework of decision level fusion, we discuss the applications of D-S evidence theory in pattern recognition. In order to solve the question of obtaining the basic belief assignment of each evidence in the practical application of D-S theory in pattern recognition, we use neural network to obtain the basic belief assignments of the uncertainties in classification.Third, in the theoretical framework of decision level fusion, in order to solve the practical problems of fuzzy logic that the existing reasoning method is too simple in calculation to preserve lots of useful information, we apply D-S theory to fuzzy reasoning. In addition, we simulate the Attention Mechanism function of human brain to treat different rules in different ways.At last, in order to further explore the practicality of fusion pattern recognition, we use multimodal biometrics, which is based on fingerprint and hand geometry to validate the availability of the third method.