Wang Ting, Dong Junyu, Sun Xin, Zhang Shu, Wang Shengke
Department of Computer Science and Technology, Ocean University of China, Songling Road 268th, Qingdao 266100, China.
Biomed Mater Eng. 2014;24(6):2751-60. doi: 10.3233/BME-141093.
Facial nerve paralysis is a common disease due to nerve damage. Most approaches for evaluating the degree of facial paralysis rely on a set of different facial movements as commanded by doctors. Therefore, automatic recognition of the patterns of facial movement is fundamental to the evaluation of the degree of facial paralysis. In this paper, a novel method named Active Shape Models plus Local Binary Patterns (ASMLBP) is presented for recognizing facial movement patterns. Firstly, the Active Shape Models (ASMs) are used in the method to locate facial key points. According to these points, the face is divided into eight local regions. Then the descriptors of these regions are extracted by using Local Binary Patterns (LBP) to recognize the patterns of facial movement. The proposed ASMLBP method is tested on both the collected facial paralysis database with 57 patients and another publicly available database named the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate that the proposed method is efficient for both paralyzed and normal faces.
面神经麻痹是一种因神经损伤而导致的常见疾病。大多数评估面瘫程度的方法依赖于医生所要求的一组不同的面部动作。因此，面部运动模式的自动识别对于面瘫程度的评估至关重要。本文提出了一种名为主动形状模型加局部二值模式（ASMLBP）的新颖方法来识别面部运动模式。首先，该方法使用主动形状模型（ASM）来定位面部关键点。根据这些点，将面部划分为八个局部区域。然后通过使用局部二值模式（LBP）提取这些区域的描述符来识别面部运动模式。所提出的ASMLBP方法在收集的包含57名患者的面瘫数据库以及另一个名为日本女性面部表情（JAFFE）的公开可用数据库上进行了测试。实验结果表明，该方法对于瘫痪面部和正常面部均有效。
