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Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding

机译:基于面部动力学分析和稀疏编码的抑郁症自动诊断

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摘要

Depression is a severe psychiatric disorder preventing a person from functioning normally in both work and daily lives. Currently, diagnosis of depression requires extensive participation from clinical experts. It has drawn much attention to develop an automatic system for efficient and reliable diagnosis of depression. Under the influence of depression, visual-based behavior disorder is readily observable. This paper presents a novel method of exploring facial region visual-based nonverbal behavior analysis for automatic depression diagnosis. Dynamic feature descriptors are extracted from facial region subvolumes, and sparse coding is employed to implicitly organize the extracted feature descriptors for depression diagnosis. Discriminative mapping and decision fusion are applied to further improve the accuracy of visual-based diagnosis. The integrated approach has been tested on the AVEC2013 depression database and the best visual-based mean absolute error/root mean square error results have been achieved.
机译:抑郁症是一种严重的精神疾病,会阻止人们在工作和日常生活中正常工作。当前,抑郁症的诊断需要临床专家的广泛参与。开发用于有效和可靠地诊断抑郁症的自动系统已引起了很多关注。在抑郁症的影响下,基于视觉的行为障碍很容易观察到。本文提出了一种探索面部区域基于视觉的非语言行为分析以自动进行抑郁症诊断的新方法。从面部区域子卷中提取动态特征描述符,并采用稀疏编码隐式组织提取的特征描述符以进行抑郁症诊断。判别映射和决策融合被应用来进一步提高基于视觉的诊断的准确性。该综合方法已经在AVEC2013抑郁数据库上进行了测试,并且获得了最佳的基于视觉的平均绝对误差/均方根误差结果。

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