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Ploner, \u201cDecoding an individual&apos;s sensitivity to pain from the multivariate analysis of EEG data,\u201d Cerebral Cortex, vol.22, no.5, pp.1118-1123, 2012. 10.1093\/cercor\/bhr186","DOI":"10.1093\/cercor\/bhr186"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] P. Werner, A. Al-Hamadi, R. Niese, S. Walter, S. Gruss, and H.C. Traue, \u201cAutomatic Pain Recognition from Video and Biomedical Signals,\u201d Proc. IEEE Conf. Pattern Recognit., 2014, pp.4582-4587, Stockholm, Schweden, 2014. 10.1109\/icpr.2014.784","DOI":"10.1109\/ICPR.2014.784"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] JMM. Jam and H. Sadjedi, \u201cA System for Detecting of Infants with Pain from Normal Infants Based on Multi-band Spectral Entropy by Infant&apos;s Cry Analysis,\u201d Proc. 2nd IEEE Conf. ICCEE, pp.72-76, Dubai, UAE, 2009. 10.1109\/iccee.2009.164","DOI":"10.1109\/ICCEE.2009.164"},{"key":"7","unstructured":"[7] K. Craig, K. Prkachin, and R. 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Taylor, \u201cActive appearance models,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.23, no.6, pp.681-685, 2001. 10.1109\/34.927467","DOI":"10.1109\/34.927467"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] A.B. Ashraf, S. Lucey, J.F. Cohn, T. Chen, Z. Ambadar, K.M. Prkachin, and P.E. Solomon, \u201cThe painful face-pain expression recognition using active appearance models,\u201d Image and vision computing, vol.27, no.12, pp.1788-1796, 2009. 10.1016\/j.imavis.2009.05.007","DOI":"10.1016\/j.imavis.2009.05.007"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] P. Lucey, J. Cohn, S. Lucey, I. Matthews, S. Sridharan, and K.M. Prkachin, \u201cAutomatically detecting pain using facial actions,\u201d Proc. IEEE Conf. ACII 2009, pp.1-8, Amsterdam, The Netherlands, 2009. 10.1109\/acii.2009.5349321","DOI":"10.1109\/ACII.2009.5349321"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] P. Lucey, J. Cohn, S. Lucey, S. Sridharan, and K.M. 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Zafeiriou, M. Pantic, and M. Pietik\u00e4inen, \u201cCapturing correlations of local features for image representation,\u201d Neurocomputing, vol.184-C, pp.99-106, 2016. 10.1016\/j.neucom.2015.07.134","DOI":"10.1016\/j.neucom.2015.07.134"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] O.M. Parkhi, A. Vedaldi, and A. Zisserman, \u201cDeep face recognition,\u201d Proc. Conf. 26th BMVC, vol.1 no.3, pp.6, Swansea, UK, 2015. 10.5244\/c.29.41","DOI":"10.5244\/C.29.41"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] C.G.M. Snoek, M. Worring, and A.W.M. Smeulders, \u201cEarly versus late fusion in semantic video analysis,\u201d Proc. ACM Conf. Multimedia 2005. pp.399-402, New York, USA, 2005. 10.1145\/1101149.1101236","DOI":"10.1145\/1101149.1101236"},{"key":"38","unstructured":"[38] L. Shen, Z. Lin, and Q. 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