[1] [1] Ruth E. 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] [2] K. 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] [3] Michael S. Lidow. Long-term effects of neonatal pain on nociceptive systems[J]. Pain, 2002, 99: 377～383

[4] [4] Pat Hummel, Monique van Dijk. Pain assessment: current status and challenges[J]. Seminars in Fetal & Neonatal Medicine, 2006, 11: 237～245

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

[6] [6] Linda Sturla Franck, Christine Miaskowski. Measurement of neonatal responses to painful stimuli a research review[J]. J. Pain and Symptom Management, 1997, 14(6): 343～378

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

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

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

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

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

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

[14] [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] [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] [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] [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] [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] [19] Sheryl Brahnam, Chao-Fa Chuang, Randall S. Sexton et al.. Machine assessment of neonatal facial expressions of acute pain[J]. Decision Support Systems, 2007, 43(4): 1242～1254

[20] [20] Chengjun Liu, Harry Wechsler. 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] [21] Yoav Freund, Robert E. 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] [22] Linlin Shen, Li Bai. Mutualboost learning for selecting Gabor features for face recognition[J]. Pattern Recognition Letters, 2006, 27(15): 1758～1767

[23] [23] 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] [24] Ye Meiying, Wang Xiaodong. Identification of chaotic optical system based on support vector machine[J]. Acta Optica Sinica, 2004, 24(7): 953～956

[25] [25] 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