Quantitative analysis of facial paralysis using GMM and dynamic kernels

Praveen, N. and Mohan, C.K. (2020) Quantitative analysis of facial paralysis using GMM and dynamic kernels. VISIGRAPP 2020, 5. pp. 173-184.

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Abstract

In this paper, the quantitative assessment for facial paralysis is proposed to detect and measure the different degrees of facial paralysis. Generally, difficulty in facial muscle movements determines the degree with which patients are affected by facial paralysis. In the proposed work, the movements of facial muscles are captured using spatio-temporal features and facial dynamics are learned using large Gaussian mixture model (GMM). Also, to handle multiple disparities occurred during facial muscle movements, dynamic kernels are used, which effectively preserve the local structure information while handling the variation across the different degree of facial paralysis. Dynamic kernels are known for handling variable-length data patterns efficiently by mapping it onto a fixed length pattern or by the selection of a set of discriminative virtual features using multiple GMM statistics. These kernel representations are then classified using a support vector machine (SVM) for the final assessment. To show the efficacy of the proposed approach, we collected the video database of 39 facially paralyzed patients of different ages group, gender, and from multiple angles (views) for robust assessment of the different degrees of facial paralysis. We employ and compare the trade-off between accuracy and computational loads for three different categories of the dynamic kernels, namely, explicit mapping based, probability-based, and matching based dynamic kernel. We have shown that the matching based kernel, which is very low in computational loads achieves better classification performance of 81.5% than the existing methods. Also, with the higher-order statistics, the probability kernel involves more communication overhead but gives significantly high classification performance of 92.46% than state-of-the-art methods.

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