Quantification of facial expressions for neuropsychiatry

描述（由申请人提供）：该项目的主要目标是开发面部表情量化的方法。面部表情分析正越来越多地用于神经精神障碍的临床研究，包括情感障碍和精神分裂症，这会导致情感的感知和表达的缺陷。然而，临床医生仍然依赖于手动的、主要是定性的并且通常具有低再现性的表达评级方法。该项目旨在开发客观和自动化的工具，这将大大增强目前可靠的临床诊断和后续行动的能力。所提出的工具执行的精细粒度的结构变形的面部表情变化过程中的形态学分析。面部将使用可变形模型表示，作为弹性区域的复杂组合，随着表情的变化而变形（膨胀和收缩）。根据研究设计，将使用中性表情或标准化模板作为参考单位，通过用于定义量化测量的高维形状变换来估计具有不同表情的两个面部之间的变形。在视频序列中，后续帧之间的形状变换将在时间上传播，从而组合空间和时间信息。这些方法将根据临床上可接受的表达评级量表进行验证，就其复制临床确定结果的能力而言，重点是量化情感障碍患者和健康对照之间的表达差异。预计该项目完成后，将向临床医生提供一套完整的表达量化工具，这将提高情感相关疾病的诊断准确性，并提供超出现有临床技术范围的量化措施。这些工具有望为神经精神科医生提供量化情感表达受损程度的能力，定量评估对药物的反应，获得暴力和攻击的行为预测因子，并在儿童，青少年和患者家庭成员中找到内表型标记物，这可能会预测未来的疾病发作。该项目的长期目标是提供可靠、客观、可重复和易于临床医生使用的表达量化方法，这将显著影响用于准确诊断导致情感表达缺陷的临床疾病的程序，如精神分裂症、情感障碍、帕金森病和老年痴呆症。

DESCRIPTION (provided by applicant): The main goal of this project is to develop methods for quantification of facial expressions. Facial expression analysis is being increasingly used in clinical investigations of neuropsychiatric disorders including affective disorders and schizophrenia, which cause deficits in the perception and expression of emotion. However, clinicians still rely on methods of expression rating that are manual, largely qualitative and typically of low reproducibility. This project seeks to develop objective and automated tools, which will significantly augment current capabilities for reliable clinical diagnosis and follow-up. The proposed tools perform a morphometric analysis of fine-grained structural deformations of the face during an expression change. Faces will be represented using deformable models, as a complex combination of elastic regions that deform (expand and contract) as the expression changes. The deformation between two faces with different expressions will be estimated through a high-dimensional shape transformation that will be used to define the quantification measure, using the neutral expression or a standardized template as reference units, depending on the study design. In video sequences, the shape transformation between subsequent frames will be temporally propagated, thereby combining the spatial and temporal information. These methods will be validated against clinically accepted scales of expression rating, in terms of their ability to replicate clinically established results, with emphasis on quantifying difference in expressions between patients with affective disorders and healthy controls. It is expected that upon completion of the project, an integrated collection of expression quantification tools will be provided to clinicians, which will improve diagnostic accuracy in affect-related disorders and provide quantification measures beyond the scope of currently existing clinical techniques. These tools are expected to provide neuropsychiatrists the ability to quantify the degree of impairment in affect expression, quantitatively assess response to medication, obtain behavioral predictors of violence and aggression and find endophenotypic markers in children, adolescents, and family members of patients, which could potentially predict the future onset of the disorder. The long-term goal of the project is to provide methods for expression quantification that are reliable, objective, reproducible and easily usable by clinicians and that will significantly influence the procedures used for accurately diagnosing clinical conditions that cause deficits in emotional expressiveness, such as schizophrenia, affective disorders, Parkinson's disease and senile dementias.