结合二维和三维特征量化精神分裂症患者面部表情异常

Peng Wang, Christiane Köhler, Fred Barrett, R. Gur, R. Gur, R. Verma
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引用次数: 23

摘要

目前大多数基于计算机的面部表情分析方法侧重于识别完美姿势的表情,因此无法处理有表情障碍的个体。特别是,精神分裂症患者通常会出现“平淡”或“不恰当”的表情受损,这使得对他们面部表情的量化成为一个具有挑战性的问题。本文提出了一种量化精神分裂症患者与健康对照组之间群体差异的方法,该方法通过提取专门的特征并分析特征流形上的群体差异。特征包括二维和三维几何特征,以及结合三维几何和二维纹理的矩不变量。演员面部表情识别实验表明,我们的组合特征比二维几何特征或纹理特征更能表征面部表情。然后将这些特征嵌入到ISOMAP歧管中,以量化对照组和患者之间的组差异。实验表明,我们的结果得到了人类评分结果和临床结果的有力支持,从而提供了一个能够量化精神分裂症患者异常的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying Facial Expression Abnormality in Schizophrenia by Combining 2D and 3D Features
Most of current computer-based facial expression analysis methods focus on the recognition of perfectly posed expressions, and hence are incapable of handling the individuals with expression impairments. In particular, patients with schizophrenia usually have impaired expressions in the form of "flat" or "inappropriate" affects, which make the quantification of their facial expressions a challenging problem. This paper presents methods to quantify the group differences between patients with schizophrenia and healthy controls, by extracting specialized features and analyzing group differences on a feature manifold. The features include 2D and 3D geometric features, and the moment invariants combining both 3D geometry and 2D textures. Facial expression recognition experiments on actors demonstrate that our combined features can better characterize facial expressions than either 2D geometric or texture features. The features are then embedded into an ISOMAP manifold to quantify the group differences between controls and patients. Experiments show that our results are strongly supported by the human rating results and clinical findings, thus providing a framework that is able to quantify the abnormality in patients with schizophrenia.
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