He Shu, Soraghan John J, O'Reilly Brian F, Xing Dongshan
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.
IEEE Trans Biomed Eng. 2009 Jul;56(7):1864-70. doi: 10.1109/TBME.2009.2017508. Epub 2009 Mar 27.
Facial paralysis is the loss of voluntary muscle movement of one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents a novel framework for objective measurement of facial paralysis. The motion information in the horizontal and vertical directions and the appearance features on the apex frames are extracted based on the local binary patterns (LBPs) on the temporal-spatial domain in each facial region. These features are temporally and spatially enhanced by the application of novel block processing schemes. A multiresolution extension of uniform LBP is proposed to efficiently combine the micropatterns and large-scale patterns into a feature vector. The symmetry of facial movements is measured by the resistor-average distance (RAD) between LBP features extracted from the two sides of the face. Support vector machine is applied to provide quantitative evaluation of facial paralysis based on the House-Brackmann (H-B) scale. The proposed method is validated by experiments with 197 subject videos, which demonstrates its accuracy and efficiency.
面瘫是指一侧面部随意肌运动丧失。一个定量、客观且可靠的评估系统对于治疗此类疾病的临床医生来说将是一个非常有价值的工具。本文提出了一种用于面瘫客观测量的新框架。基于每个面部区域时空域上的局部二值模式（LBP），提取水平和垂直方向的运动信息以及顶点帧上的外观特征。通过应用新颖的块处理方案，这些特征在时间和空间上得到增强。提出了均匀LBP的多分辨率扩展，以有效地将微模式和大规模模式组合成一个特征向量。通过从面部两侧提取的LBP特征之间的电阻平均距离（RAD）来测量面部运动的对称性。应用支持向量机基于House-Brackmann（H-B）量表对面瘫进行定量评估。通过对197个受试者视频的实验验证了所提出的方法，证明了其准确性和效率。
