Deep convolution network based emotion analysis towards mental health care
Deep convolution network based emotion analysis towards mental health care
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DOI：
10.1016/j.neucom.2020.01.034
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发表时间：
2020-05-07
期刊：
影响因子：
6
通讯作者：
Zhou, Huiyu
中科院分区：
文献类型：
--
作者：
Fei, Zixiang;Yang, Erfu;Zhou, Huiyu
面部表情在交流过程中发挥着重要作用，它可以传达和推断有关个人情绪状态的信息。研究表明，自动面部表情识别是一种很有前途的精神保健查询途径，因为面部表情也可以反映个人的精神状态。为了开发用户友好、低成本和有效的面部表情分析系统用于心理健康护理，本文提出了一种新的基于深度卷积网络的情感分析框架，以支持心理状态检测和诊断。该系统能够处理面部图像，并通过一种新的解决方案来解释情绪的时间演变，其中从AlexNet的全连接层6中提取深度特征，并利用标准的线性判别分析分类器来获得最终的分类结果。它针对5个基准数据库进行了测试，包括JAFFE，KDEF，CK+以及FER2013和AffectNet等“野外”图像数据库。与其他国家的最先进的方法相比，我们观察到，我们的方法具有整体较高的准确率的面部表情识别。此外，与Vgg16、GoogleNet、ResNet和AlexNet等最先进的深度学习算法相比，所提出的方法具有更高的效率和更低的设备要求。本文中提出的实验表明，所提出的方法优于其他方法的准确性和效率，这表明它可以作为一个智能，低成本，用户友好的认知辅助检测，监测和诊断患者的心理健康，通过自动面部表情分析。(C)2020由Elsevier B.V.出版
Facial expressions play an important role during communications, allowing information regarding the emotional state of an individual to be conveyed and inferred. Research suggests that automatic facial expression recognition is a promising avenue of enquiry in mental healthcare, as facial expressions can also reflect an individual's mental state. In order to develop user-friendly, low-cost and effective facial expression analysis systems for mental health care, this paper presents a novel deep convolution network based emotion analysis framework to support mental state detection and diagnosis. The proposed system is able to process facial images and interpret the temporal evolution of emotions through a new solution in which deep features are extracted from the Fully Connected Layer 6 of the AlexNet, with a standard Linear Discriminant Analysis Classifier exploited to obtain the final classification outcome. It is tested against 5 benchmarking databases, including JAFFE, KDEF,CK+, and databases with the images obtained 'in the wild' such as FER2013 and AffectNet. Compared with the other state-of-the-art methods, we observe that our method has overall higher accuracy of facial expression recognition. Additionally, when compared to the state-of-the-art deep learning algorithms such as Vgg16, GoogleNet, ResNet and AlexNet, the proposed method demonstrated better efficiency and has less device requirements. The experiments presented in this paper demonstrate that the proposed method outperforms the other methods in terms of accuracy and efficiency which suggests it could act as a smart, low-cost, user-friendly cognitive aid to detect, monitor, and diagnose the mental health of a patient through automatic facial expression analysis. (C) 2020 Published by Elsevier B.V.