Facial paralysis is a common clinical condition occurring in 30 to 40 patients per 100,000 people per year in Japan. A quantitative tool to support medical diagnostics is necessary. This paper presents a technique that we combined Gabor filters and wavelet decomposition to develop this tool. In our work, the Gabor filters and the wavelet decomposition are used as preprocessing steps to extract the feature. These features are used as the inputs of a multi-class support vector machines for quantitative evaluation of facial paralysis. Our method overcomes the drawbacks of the other techniques such as noisy removal and against variation of illumination. Experimental results show that our proposed method outperforms other conventional techniques testing on a dynamic facial expression image database.