Facial expression recognition for monitoring neurological disorders based on convolutional neural network.
Facial expression recognition for monitoring neurological disorders based on convolutional neural network.
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DOI：
10.1007/s11042-019-07959-6
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发表时间：
2019-11
影响因子：
3.6
通讯作者：
Bunyak, Filiz
中科院分区：
文献类型：
--
作者：
Yolcu, Gozde;Oztel, Ismail;Kazan, Serap;Oz, Cemil;Palaniappan, Kannappan;Lever, Teresa E.;Bunyak, Filiz
关键词：
面部表情是非语言交流的重要组成部分。识别神经系统疾病患者的面部表情至关重要，因为这些人可能已经失去了大量的语言交流能力。这样的评估需要涉及医务人员的耗时检查，这可能相当具有挑战性和昂贵。低成本、无创的自动面部表情识别系统可以帮助专家检测神经系统疾病。在这项研究中，使用一种新颖的深度学习方法开发了一个自动面部表情识别系统。该体系结构由四级网络组成。第一、第二和第三网络对面部表情识别所必需的面部成分进行分割。利用这三种网络，得到了一幅象形化的人脸图像。第四个网络使用原始面部图像对面部表情进行分类并对面部图像进行图示化。该方法将面部整体信息与局部特征相结合，增强了面部表情识别的鲁棒性。初步实验结果表明，基于RaFD数据库的面部表情识别准确率达到94.44%。该系统比使用原始图像的面部表情识别系统提高了5%。本研究提出了一种定量、客观、无创的面部表情识别系统，以帮助监测和诊断影响面部表情的神经系统疾病。
Facial expressions are a significant part of non-verbal communication. Recognizing facial expressions of people with neurological disorders is essential because these people may have lost a significant amount of their verbal communication ability. Such an assessment requires time consuming examination involving medical personnel, which can be quite challenging and expensive. Automated facial expression recognition systems that are low-cost and noninvasive can help experts detect neurological disorders. In this study, an automated facial expression recognition system is developed using a novel deep learning approach. The architecture consists of four-stage networks. The first, second and third networks segment the facial components which are essential for facial expression recognition. Owing to the three networks, an iconize facial image is obtained. The fourth network classifies facial expressions using raw facial images and iconize facial images. This four-stage method combines holistic facial information with local part-based features to achieve more robust facial expression recognition. Preliminary experimental results achieved 94.44% accuracy for facial expression recognition on RaFD database. The proposed system produced 5% improvement than the facial expression recognition system by using raw images. This study presents a quantitative, objective and non-invasive facial expression recognition system to help in the monitoring and diagnosis of neurological disorders influencing facial expressions.
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DOI：
10.1002/aur.1508
发表时间：
2016-02
期刊：
Autism research : official journal of the International Society for Autism Research
影响因子：
--
作者：
Brewer R;Biotti F;Catmur C;Press C;Happé F;Cook R;Bird G
通讯作者：
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影响因子：
3.9
作者：
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影响因子：
2.6
作者：
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Black, SE
DOI：
10.3390/s130607714
发表时间：
2013-06-14
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Ghimire D;Lee J
通讯作者：
Lee J
影响因子：
3.6
作者：
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通讯作者：
Dharma, Dejey