Abstract
A method to extract the fractal features for fingerprint of medicinal herbs based on wavelet transform, which is called fractal-wavelet features, was presented. The method has antijamming property against the change of sample concentration. However, the recognition rate based on fractal-wavelet features is not satisfied when fingerprint of medicinal herbs has some slight changes of concentrations, the number of peak and peak drift of sample are processed in specially situation. This paper proposes the fractal features for glycyrrhiza fingerprint of medicinal herbs using empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMF) from high to low frequency. Then EMD fractal features in fingerprint of medicinal herbs are extracted through computing the fractal dimensions of every IMF. The novel approach is applied to recognition of the three types of glycyrrhiza fingerprints of medicinal herbs. Experiments show that EMD fractal features have better recognition rate than that of the traditional ones in case of the concentration-change, i.e. the number of peak and peak drift of sample have slight changes.
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