人脸疼痛表情识别综述
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摘要
自动疼痛识别技术在医疗保健,特别是在对无法用语言表达疼痛的病人的治疗和护理中具有广泛的应用前景,因此逐步受到研究者的关注。由于人的面部线索是很重要的疼痛评估依据,并且基于计算机视觉技术的人脸表情识别研究已取得很大进展,因此利用面部表情信息实现自动疼痛识别成为了一条有效的途径。本文首先简要介绍了目前常用的STOIC表情数据库、婴儿疼痛表情分类(COPE)数据库、UNBC-McMaster肩部疼痛数据库和BioVid热疼痛数据库,然后从静态图像疼痛表情识别、视频序列疼痛表情识别、特定人物疼痛识别以及多信息融合疼痛识别4个方面对近10年的疼痛表情识别主要方法进行了详细的介绍,最后对目前人脸疼痛表情识别现状进行总结和分析,并阐述了其存在的挑战和未来的发展方向。
In recent years,research on automatic pain recognition is of increased attetion,due to its wide application in clinics,especially for the treatment and nursing of patients who cannot express their pain vocally.Since the face is the vital cue for evaluating pain and the great progress has been made in facial expression recognition with computer vison technique,it is an effective way to recognize pain automatically utilizing facial information.Here,four existing databases used for pain recognition are firstly introduced,namely,the STOIC database,the Infant COPE database,the UNBC-McMaster Shoulder Pain Expression Archive database,and the BioVid Heat Pain databse.Then,the proposed methods in the last decade can be divided into four categories depending on the use of either static images,video sequences,person specific strategy or multimodal methods.Finally,the current state of the art in pain detection research,open issues and future directions are highlighted.
In recent years,research on automatic pain recognition is of increased attetion,due to its wide application in clinics,especially for the treatment and nursing of patients who cannot express their pain vocally.Since the face is the vital cue for evaluating pain and the great progress has been made in facial expression recognition with computer vison technique,it is an effective way to recognize pain automatically utilizing facial information.Here,four existing databases used for pain recognition are firstly introduced,namely,the STOIC database,the Infant COPE database,the UNBC-McMaster Shoulder Pain Expression Archive database,and the BioVid Heat Pain databse.Then,the proposed methods in the last decade can be divided into four categories depending on the use of either static images,video sequences,person specific strategy or multimodal methods.Finally,the current state of the art in pain detection research,open issues and future directions are highlighted.
引文
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[26]Ashraf A B,Lucey S,Cohn J F,et al.The painful face-pain expression recognition using active appearance models[J].Image and Vision Computing,2009,27(12):1788-1796.
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[33]Pedersen H.Learning appearance features for pain detection using the UNBC-McMaster shoulder pain expression archive database[C]∥The 10th International Conference on Computer Vision Systems.Copenhagen,Denmark:Springer,2015:128-136.
[34]Sikka K,Dhall A,Bartlett M.Weakly supervised pain localization using multiple instance learning[C]∥The 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition(FG).Shanghai,China:IEEE Computer Society,2013:1-8.
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[40]卢官明,李晓南,李海波.新生儿疼痛面部表情识别方法的研究[J].光学学报,2008,28(11):2109-2114.Lu Guanming,Li Xiaonan,Li Haibo.Research on recognition for facial expression of pain in neonates[J].Acta Optica Sinica,2008,28(11):2109-2114.
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[44]Bartlett M S,Littlewort G C,Frank M G,et al.Automatic recognition of facial actions in spontaneous expressions[J].Journal of Multimedia,2006,1(6):22-35.
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