面部表情
心情
模态(人机交互)
回归
萧条(经济学)
模式
人工智能
表达式(计算机科学)
计算机科学
语音识别
回归分析
直方图
心理学
模式识别(心理学)
机器学习
认知心理学
临床心理学
心理治疗师
宏观经济学
社会学
经济
图像(数学)
程序设计语言
社会科学
作者
Hongying Meng,Di Huang,Heng Wang,Hongyu Yang,Mohammed AI-Shuraifi,Yunlong Wang
标识
DOI:10.1145/2512530.2512532
摘要
Depression is a typical mood disorder, and the persons who are often in this state face the risk in mental and even physical problems. In recent years, there has therefore been increasing attention in machine based depression analysis. In such a low mood, both the facial expression and voice of human beings appear different from the ones in normal states. This paper presents a novel method, which comprehensively models visual and vocal modalities, and automatically predicts the scale of depression. On one hand, Motion History Histogram (MHH) extracts the dynamics from corresponding video and audio data to represent characteristics of subtle changes in facial and vocal expression of depression. On the other hand, for each modality, the Partial Least Square (PLS) regression algorithm is applied to learn the relationship between the dynamic features and depression scales using training data, and then predict the depression scale for an unseen one. Predicted values of visual and vocal clues are further combined at decision level for final decision. The proposed approach is evaluated on the AVEC2013 dataset and experimental results clearly highlight its effectiveness and better performance than baseline results provided by the AVEC2013 challenge organiser.
科研通智能强力驱动
Strongly Powered by AbleSci AI