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的量化方法。面部表情分析越来越多地用于神经精神疾病的临床研究，包括情感障碍和精神分裂症，这些疾病会导致情感感知和表达的缺陷。然而，临床医生仍然依赖于表达评级的方法是手动的，很大程度上是定性的，通常是低重复性的。该项目旨在开发客观和自动化的工具，这将大大提高目前可靠的临床诊断和随访的能力。所提出的工具在表情变化期间对面部的细粒度结构变形进行形态计量分析。面部将使用可变形模型来表示，作为弹性区域的复杂组合，随着表情的变化而变形（扩展和收缩）。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，通过高维形状变换来估计具有不同表情的两个面孔之间的变形，该形状变换将用于定义量化度量。在视频序列中，后续帧之间的形状变换会进行时间传播，从而将时空信息结合起来。这些方法将根据临床接受的表达等级量表进行验证，就其复制临床建立的结果的能力而言，重点是量化情感障碍患者和健康对照组之间的表达差异。预计项目完成后，将为临床医生提供一套完整的表达量化工具，提高对情感相关疾病的诊断准确性，并提供超出目前临床技术范围的量化措施。这些工具有望为神经精神病学家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素，并在儿童、青少年和患者家庭成员中找到可能预测未来发病的内表型标记。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，并将显著影响用于准确诊断导致情感表达缺陷的临床条件的程序，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.