No abstract

Facial paralysis is a common clinical condition with the rate from 20 to 25 patients per 100,000 people per year. An objectively quantitative tool to support for medical diagnostics is very necessary and important. This paper proposes a very robust method that overcomes the drawbacks of other techniques to develop this tool. In our research, we use a combination of local binary patterns (LBP) and Gabor filters to calculate the features that are used for training and testing. Experiments show that our results outperform other techniques testing on a dynamic facial expression database.

Facial paralysis is a common clinical condition with the rate from 20 to 25 patients per 100,000 people per year. An objectively quantitative tool to support for medical diagnostics is very necessary and important. This paper proposes a very simple, visual, and highly efficient method that overcomes the drawbacks of other methods to develop this tool. In our research, we use the tracking of interest points to measure the features that are used for training and testing. Experiments show that our method outperforms other techniques testing on a dynamic facial expression database.

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.

Abstract-Facial paralysis is a common clinical condition occurring in the rate 20 to 25 patients per 100,000 people per year. An objective quantitative tool to support medical diagnostics is necessary. This paper proposes a robust method that decomposes the images into multi frequencies-space domain, and then features are extracted for classification using a support vector machine (SVM). The method analyses the images in frequencyspace domain, so it overcomes the problems of other techniques such as the change of illumination, noise and redundant frequencies. Experiments show that our proposed method outperforms other techniques testing on a dynamic facial expression image database.