Paper Currency Recognition using Gaussian Mixture Models Based on Structural Risk Minimization
Fanhui Kong, Jiquan Ma, Jiafeng Liu · 2006
Gaussian Mixture Model (GMM) is a popular tool for density estimation. The parameters of the GMM are estimated based on Maximum Likelihood principle (MLP) in almost all recognition system. However, the number of mixtures used in the model is important for determining the model's effectiveness; the general problem of mixture modeling is difficult when the number of components is unknown. This paper presents paper currency recognition using GMM based on Structural Risk Minimization (SRM). By selecting the proper number of the components with SRM, the system can overcome the demerit by the number of the Gaussian components selected artificially. A total number of 8 bill types including 5, 10(new and old model), 20, 50(new and old model), 100(new and old model) are considered as classification categories. The experiments show that GMM which employs SRM is a more flexible alternative and lead to improved results for Chinese paper currency recognition.