In the model-based recognition methods, the result's confidence is decided by the feature distance between the segmented region and the target model, and can be defined as the posterior probability that can be computed from the object and background's prior probability and conditional probability with Bayesian formula. However, when recognizing the target, many physical constrains, or image measurements of object region and background region, can be applied on the validation of the recognition result, and should be introduced into the confidence analysis. In this paper, we proposed a new method to analyze the target recognition quality by combining the physical constrains or prior knowledge into confidence analysis within the frame of mathematical statistic theory and Dempster-Shafer's evidence theory. In this method, the usability of the information sources is appraised with Kolmogorov-Smirnov test method and the different computation models to compute the belief value to classifier's result corresponding to the different information source types were also proposed. The method was tested on the real sequences of images, and the result indicated that the proposed method for confidence analysis is feasible and effective.