光学学报, 2008, 28 (11): 2109, 网络出版: 2008-11-17   

新生儿疼痛面部表情识别方法的研究

Research on Recognition for Facial Expression of Pain in Neonates
作者单位
1 南京邮电大学 通信与信息工程学院, 江苏 南京 210003
2 南京医科大学附属南京儿童医院, 江苏 南京 210008
3 瑞典于默奥大学应用物理与电子系, S-901 87 Ume, Sweden
引用该论文

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.

Lu Guanming, Li Xiaonan, Li Haibo. Research on Recognition for Facial Expression of Pain in Neonates[J]. Acta Optica Sinica, 2008, 28(11): 2109.

参考文献

[1] . Grunau, Liisa Holsti, Jeroen W. B. Peters. Long-term consequences of pain in human neonates[J]. Seminars in Fetal & Neonatal Medicine, 2006, 11: 268-275.

[2] . J. S. Anada, V. Coskun, K. V. Thrivikraman et al.. Long-term behavioral effects of repetitive pain in neonatal rat pups[J]. Physiology & Behavior, 1999, 66(4): 627-637.

[3] . Lidow. Long-term effects of neonatal pain on nociceptive systems[J]. Pain, 2002, 99: 377-383.

[4] . Pain assessment: current status and challenges[J]. Seminars in Fetal & Neonatal Medicine, 2006, 11: 237-245.

[5] . J. W. Bours, Bonnie Stevens et al.. Assessment of pain in the neonate[J]. Seminars in Perinatology, 1998, 22(5): 402-416.

[6] . Measurement of neonatal responses to painful stimuli a research review[J]. J. Pain and Symptom Management, 1997, 14(6): 343-378.

[7] . Duhn, Jennifer M. Medves. A systematic integrative review of infant pain assessment tools[J]. Advances in Neonatal Care, 2004, 4(3): 126-140.

[8] . An exploration of nurses′ knowledge of, and attitudes towards, pain recognition and management in neonates[J]. J. Neonatal Nursing, 2005, 11: 65-71.

[9] . Fasel, Juergen Luettin. Automatic facial expression analysis: a survey[J]. Pattern Recognition, 2003, 36(1): 259-275.

[10] . Inferring facial expressions from videos: Tool and application[J]. Signal Processing: Image Communication, 2007, 22(9): 769-784.

[11] . Xiang, M. K. H. Leung, S. Y. Cho. Expression recognition using fuzzy spatio-temporal modeling[J]. Pattern Recognition, 2008, 41(1): 204-216.

[12] Irene Kotsia, Ioannis Pitas. Facial expression recognition in image sequences using geometric deformation features and support vector machines[C]. IEEE Transactions on Image Processing, 2007, 16(1): 172~187

[13] . Sebe, M. S. Lew, Y. Sun et al.. Authentic facial expression analysis[J]. Image and Vision Computing, 2007, 25(12): 1856-1863.

[14] Jun Wang, Lijun Yin. Static topographic modeling for facial expression recognition and analysis[J]. Computer Vision and Image Understanding, 2007, 108(1~2): 19~34

[15] Takeo Kanade, Jeffrey F. Cohn, Yingli Tian. Comprehensive database for facial expression analysis[C]. IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 2000. 46~53

[16] Ying-li Tian, Takeo Kanade, Jeffrey F. Cohn. Recognizing action units for facial expression analysis[C]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 97~115

[17] Michael Lyons, Shigeru Akamatsu, Miyuki Kamachi et al.. Coding facial expressions with Gabor wavelets[C]. IEEE International Conference on Automatic Face and Gesture Recognition, 1998, A(14~16). 200~205

[18] Paul Ekman, Wallace V. Friesen. Facial Action Coding System: A Technique for the Measurement of Facial Movement[M]. Palo Alto, Calif.: Consulting Psychologists Press, 1978

[19] . Sexton et al.. Machine assessment of neonatal facial expressions of acute pain[J]. Decision Support Systems, 2007, 43(4): 1242-1254.

[20] . Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]. IEEE Transactions on Image Processing, 2002, 11(4): 467-476.

[21] . Schapire. A decision-theoretic generalization of on-line learning and an application to boosting[J]. J. Computer and System Sciences, 1997, 55(1): 119-139.

[22] . Mutualboost learning for selecting Gabor features for face recognition[J]. Pattern Recognition Letters, 2006, 27(15): 1758-1767.

[23] 陈全胜, 赵杰文, 张海东. 基于支持向量机的近红外光谱鉴别茶叶的真伪[J]. 光学学报, 2006, 26(6): 933~937

    Chen Quansheng, Zhao Jiewen, Zhang Haidong. Identification of authe nticity of tea with near infrared spectroscopy based on support vector machine[J] . Acta Optica Sinica, 2006, 26(6): 933~937

[24] 叶美盈, 汪晓东. 混沌光学系统辨识的支持向量机方法[J]. 光学学报, 2004, 24(7): 953~956

    Ye Meiying, Wang Xiaodong. Identification of chaotic optical system based on support vector machine[J]. Acta Optica Sinica, 2004, 24(7): 953~956

[25] 李素梅, 韩应哲, 张延炘. 基于支持向量机的非线性荧光光谱的识别[J]. 光学学报, 2006, 26(1): 147~151

    Li Sumei, Han Yingzhe, Zhang Yanxin. Recognition of nonlinear fluorescence spect rum of support vector machine networks[J] . Acta Optica Sinica, 2006, 26(1): 147~151

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. Lu Guanming, Li Xiaonan, Li Haibo. Research on Recognition for Facial Expression of Pain in Neonates[J]. Acta Optica Sinica, 2008, 28(11): 2109.

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