Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2016, Pages 1535-1543

  Zhou, Jing ^a,b   Hong, Xiaopeng ^a   Su, Fei ^b   Zhao, Guoying ^a  

^a UNIVERSITY OF OULU   (Finland)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; NEURAL NETWORKS; PATTERN RECOGNITION; REGRESSION ANALYSIS;

ADJACENT FRAMES; CONVOLUTIONAL NEURAL NETWORK; INTENSITY ESTIMATION; MEDICAL FIELDS; RECURRENT NETWORKS; SHOULDER PAIN; SLIDING-WINDOW STRATEGIES; VECTOR SEQUENCES;

HEALTH;

EID: 85010198893     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2016.191     Document Type: Conference Paper

References (37)

1. M. Adibuzzaman, C. Ostberg, S. Ahamed, R. Povinelli, B. Sindhu, R. Love, F. Kawsar, and G. M. T. Ahsan. Assessment of pain using facial pictures taken with a smartphone. In Computer Software and Applications Conference (COMPSAC), 2015 IEEE 39th Annual, volume 2, pages 726-731. IEEE, 2015
2. A. B. Ashraf, S. Lucey, J. F. Cohn, T. Chen, Z. Ambadar, K. M. Prkachin, and P. E. Solomon. The painful face-pain expression recognition using active appearance models. Image and vision computing, 27(12):1788-1796, 2009
3. F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio. Theano: new features and speed improvements; 2012. In Deep Learning and Unsu pervised Feature Learning NIPS 2012 Workshop
4. J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio. Theano: A cpu and gpu math expression compiler. In Proceedings of the Python for scientific computing conference (SciPy), volume 4, page 3. Austin, TX, 2010
5. G. A. Carpenter and S. Grossberg. A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer vision, graphics, and image processing, 37(1):54-115, 1987
6. J. Chen, X. Liu, P. Tu, and A. Aragones. Person-specific expression recognition with transfer learning. In Image Processing (ICIP), 2012 19th IEEE International Conference on, pages 2621-2624. IEEE, 2012
7. T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. IEEE Transactions on Pattern Analysis & Machine Intelligence, (6):681-685, 2001
8. P. Ekman,W. Friesen, and J. Hager. Facial action coding system: Research nexus. 2002. Network Research Information, Salt Lake City, UT, USA
9. J. L. Elman. Finding structure in time. Cognitive science, 14(2):179-211, 1990
10. B. Fernandez, A. G. Parlos, and W. K. Tsai. Nonlinear dynamic system identification using artificial neural networks (anns). In Neural Networks, 1990., 1990 IJCNN International Joint Conference on, pages 133-141. IEEE, 1990
11. C. Florea, L. Florea, and C. Vertan. Learning pain from emotion: transferred hot data representation for pain intensity estimation. In Computer Vision-ECCV 2014 Workshops, pages 778-790. Springer, 2014
12. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580-587, 2014
13. A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber. A novel connectionist system for unconstrained handwriting recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(5):855-868, 2009
14. A. Graves, A.-r. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 6645-6649. IEEE, 2013
15. Z. Hammal and M. Kunz. Pain monitoring: A dynamic and context-sensitive system. Pattern Recognition, 45(4):1265-1280, 2012
16. X. Hong, G. Zhao, S. Zafeiriou, M. Pantic, and M. Pietikäinen. Capturing correlations of local features for image representation. Neurocomputing, 184:99-106, 2016
17. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. ArXiv preprint arXiv:1502.03167, 2015
18. S. Kaltwang, O. Rudovic, and M. Pantic. Continuous pain intensity estimation from facial expressions. In Advances in Visual Computing, pages 368-377. Springer, 2012
19. R. A. Khan, A. Meyer, H. Konik, and S. Bouakaz. Pain detection through shape and appearance features. In Multimedia and Expo (ICME), 2013 IEEE International Conference on, pages 1-6. IEEE, 2013
20. M. Liang and X. Hu. Recurrent convolutional neural network for object recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3367-3375, 2015
21. P. Lucey, J. Cohn, S. Lucey, I. Matthews, S. Sridharan, and K. M. Prkachin. Automatically detecting pain using facial actions. In Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on, pages 1-8. IEEE, 2009
22. P. Lucey, J. F. Cohn, S. Lucey, S. Sridharan, and K. M. Prkachin. Automatically detecting action units from faces of pain: Comparing shape and appearance features. In Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on, pages 12-18. IEEE, 2009
23. P. Lucey, J. F. Cohn, I. Matthews, S. Lucey, S. Sridharan, J. Howlett, and K. M. Prkachin. Automatically detecting pain in video through facial action units. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 41(3):664-674, 2011
24. P. Lucey, J. F. Cohn, K. M. Prkachin, P. E. Solomon, S. Chew, and I. Matthews. Painful monitoring: Automatic pain monitoring using the unbc-mcmaster shoulder pain expression archive database. Image and Vision Computing, 30(3):197-205, 2012
25. P. Lucey, J. F. Cohn, K. M. Prkachin, P. E. Solomon, and I. Matthews. Painful data: The unbc-mcmaster shoulder pain expression archive database. In Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on, pages 57-64. IEEE, 2011
26. O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. Proceedings of the British Machine Vision, 1(3):6, 2015
27. H. Pedersen. Learning appearance features for pain detection using the unbc-mcmaster shoulder pain expression archive database. In Computer Vision Systems, pages 128-136. Springer, 2015
28. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011
29. P. Pinheiro and R. Collobert. Recurrent convolutional neural networks for scene labeling. In International Conference on Machine Learning, pages 82-90, 2014
30. K. M. Prkachin. The consistency of facial expressions of pain: A comparison across modalities. Pain, 51(3):297-306, 1992
31. K. M. Prkachin and P. E. Solomon. The structure, reliability and validity of pain expression: Evidence from patients with shoulder pain. Pain, 139(2):267-274, 2008
32. N. Rathee and D. Ganotra. A novel approach for pain intensity detection based on facial feature deformations. Journal of Visual Communication and Image Representation, 33:247-254, 2015
33. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-9, 2015
34. G. Tzimiropoulos and M. Pantic. Optimization problems for fast aam fitting in-the-wild. In Proceedings of the IEEE International Conference on Computer Vision, pages 593-600, 2013
35. N.Wang, S. Li, A. Gupta, and D.-Y. Yeung. Transferring rich feature hierarchies for robust visual tracking. ArXiv preprint arXiv:1501.04587, 2015
36. P. J. Werbos. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10):1550-1560, 1990
37. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In Proc. ECCV 2014, 2014.