Cite

I. Cohen, N. Sebe, A. Garg, et al, “Facial expression detection from video sequences: temporal and static modeling”, Computer Vision and Image Understanding, vol.1, No.91, 2003, pp.160-187.10.1016/S1077-3142(03)00081-X Search in Google Scholar

S. Morishima and H. Harashima, “Emotion space for analysis and synthesis of facial expression”, Proc. 2nd IEEE Int. Workshop on Robot and Human Communication, 1993, pp. 188-193. Search in Google Scholar

C. Shan, S. Gong and P. W. McOwan, “Robust facial expression detection using local binary patterns”, Proc. Int. Conf. on Image Processing, ICIP 2005, vol.2, No.2, 2005, pp.370-373. Search in Google Scholar

P. S. Aleksic and A. K. Katsaggelos, “Automatic facial expression detection using facial animation parameters and multistream HMMs”, IEEE Transactions on Information Forensics and Security, vol.1, No.1, 2006, pp. 3-11.10.1109/TIFS.2005.863510 Search in Google Scholar

N. Neggaz, M. Besnassi and A. Benyettou, “Facial expression detection”, Journal of Applied Sciences, vol.15, No.10, 2010, pp. 1572-1579.10.3923/jas.2010.1572.1579 Search in Google Scholar

Y. Q. Wang and , L. Liu, “New intelligent classification method based on improved meb algorithm”, International Journal on Smart Sensing and Intelligent Systems, vol. 07, No. 1, 2014, pp. 72-95.10.21307/ijssis-2017-646 Search in Google Scholar

G. Zhao and M. Pietikäinen, “Boosted multi-resolution spatiotemporal descriptors for facial expression detection”, Pattern detection letters, vol. 12, No. 30, 2009, pp. 1117-1127.10.1016/j.patrec.2009.03.018 Search in Google Scholar

F. Y. Shih, C. F. Chuang and P. S. P. Wang, “Performance comparisons of facial expression detection in JAFFE database”, Int. J. Pattern Detection and Artificial Intelligence, vol.03, No.22, 2008, pp.445-459.10.1142/S0218001408006284 Search in Google Scholar

S. Y. Fu, G. S. Yang and X. K. Kuai, “A spiking neural network based cortex-like mechanism and application to facial expression detection”, Computational Intelligence and Neuroscience, 2012, pp.1-13. Online publication date: 1-Jan-2012.10.1155/2012/946589 Search in Google Scholar

C. Shan, S. Gong and P. W. McOwan, “Facial expression detection based on local binary patterns: A comprehensive study”, Image and Vision Computing, vol.06, No.27, 2009, pp. 803816.10.1016/j.imavis.2008.08.005 Search in Google Scholar

D. C. Turk, C. Dennis and R. Melzack,” The measurement of pain and the assessment of people experiencing pain”, Handbook of Pain Assessment, ed D. C. Turk and R. Melzack, New York: Guilford, 2nd edition, 2001, pp. 1-11. Search in Google Scholar

L. Wang, R. F. Li, and K. Wang, “A novel automatic facial expression detection method based on AAM”, Journal of Computers, vol.03, No.9, 2014, pp.608-617.10.4304/jcp.9.3.608-617 Search in Google Scholar

K. M. Prkachin, “The consistency of facial expressions of pain: a comparison across modalities”, Pain, vol. 05, No.3, 1992, pp.297-306.10.1016/0304-3959(92)90213-U Search in Google Scholar

K. M. Prkachin and P. E. Solomon, “The structure, reliability and validity of pain expression: Evidence from patients with shoulder pain”, Pain, vol.139, No.2, 2008, pp.267-274.10.1016/j.pain.2008.04.010 Search in Google Scholar

S. J. Zhang, B. Jiang and T. Wang, “Facial expression detection algorithm based on active shape model and gabor wavelet”, Journal of Henan University (Natural Science), vol.05, No.40, 2010, pp.521-524. Search in Google Scholar

W. Zhang and L. M. Xia, “Pain expression detection based on SLPP and MKSVM”, Int. J. Engineering and Manufacturing, No.3, 2011, pp. 69-74.10.5815/ijem.2011.03.11 Search in Google Scholar

K. W. Wan, K. M. Lam and K. C. Ng, “An accurate active shape model for facial feature extraction”, Pattern Detection Letters, vol.15, No.26, 2005, pp. 2409-2423.10.1016/j.patrec.2005.04.015 Search in Google Scholar

J. M. Lobo and M. F. Tognelli, “Exploring the effects of quantity and location of pseudoabsences and sampling biases on the performance of distribution models with limited point occurrence data”, Journal for Nature Conservation, vol. 19, No.1, 2011, pp.1-7.10.1016/j.jnc.2010.03.002 Search in Google Scholar

S. M. Bhandarkar and X. Luo, “Integrated and tracking of multiple faces using particle filtering and optical flow-based elastic matching”, Computer Vision and Image Understanding, vol. 06, No.113, 2009, pp. 708-725.10.1016/j.cviu.2008.11.010 Search in Google Scholar

B. K. Horn and B. G. Schunck, “Determining optical flow”, Artificial Intelligence, No.17, 1981, pp.185- 204.10.1016/0004-3702(81)90024-2 Search in Google Scholar

G. J. Burghouts and K. Schutte, “Spatio-temporal layout of human actions for improved bag- of-words action “, Pattern Detection Letters, vol.15, No.34, 2013, pp.1861-1869.10.1016/j.patrec.2013.01.024 Search in Google Scholar

J. D. Keeler, D. E. Rumelhart and W. K. Leow, “Integrated segmentation and detection of hand-printed numerals”, 1990 NIPS-3: Proc. Conf. on Advances in neural information processing systems 3, San Francisco, CA, USA: Morgan Kaufmann Publishers Inc, 1990, pp. 557–563. Search in Google Scholar

T. G. Dietterich, R. H. Lathrop and T. Lozano-Perez, “Solving the multiple instance problem with axis-parallel rectangles”, Artificial Intelligence, No.89, 1997, pp. 31-71.10.1016/S0004-3702(96)00034-3 Search in Google Scholar

A. Zafra, M. Pechenizkiy, and S. Ventura, “Relief-MI: an extension of relief to multiple instance learning”, Neurocomputing, No.75, 2012, pp.210-218.10.1016/j.neucom.2011.03.052 Search in Google Scholar

Y. X. Chen, J. B. Bi and J. Z. Wang, “MILES: multiple-instance learning via embedded instance selection”, IEEE Transaction Pattern Analysis and Machine Intelligence, No.28, 2006, pp. 1931-47.10.1109/TPAMI.2006.24817108368 Search in Google Scholar

X. F. Song, L. C. Jiao, S. Y. Yang, X. R. Zhang, and F. H. Shang, “Sparse coding and classifier ensemble based multi-instance learning for image categorization”, Signal Processing, No.93, 2013, pp.1-11.10.1016/j.sigpro.2012.07.029 Search in Google Scholar

P. Viola, J. Platt, and C. Zhang, “Multiple instance boosting for object “, Advance in Neutral Information Processing System, No.18, 2006, pp.1419-1426. Search in Google Scholar

M. Nakamura, H. Nomiya and K. Uehara, “Improvement of boosting algorithm by modifying the weighting rule”, Annals of Mathematics and Artificial Intelligence, vol.1, No.41, 2004, pp. 95-109.10.1023/B:AMAI.0000018577.32783.d2 Search in Google Scholar

T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez, “Solving the multiple instance problem with axis-parallel rectangles”, Artificial Intelligence, vol.89, No.1, 1997, pp.31–71.10.1016/S0004-3702(96)00034-3 Search in Google Scholar

S. Andrews and T. Hofmann, “Multiple instance learning via disjunctive programming boosting”, Advances in Neural Information Processing Systems, No.16, 2004, pp. 65-72. Search in Google Scholar

T. Quazi, S.C. Mukhopadhyay, N. Suryadevara and Y. M. Huang, Towards the Smart Sensors Based Human Emotion Recognition, Proceedings of IEEE I2MTC 2012 conference, IEEE Catalog number CFP12MT-CDR, ISBN 978-1-4577-1771-0, May 13-16, 2012, Graz, Austria, pp. 2365-2370. Search in Google Scholar

Y. T. Chen, C. S. Chen, Y. P. Hung ,et al, “Multi-class multi-instance boosting for partbased human “, IEEE 12th Int. Conf. on. Computer Vision Workshops (ICCV Workshops), 2009, pp.1177-1184.10.1109/ICCVW.2009.5457475 Search in Google Scholar

G.Sengupta, T.A.Win, C.Messom, S.Demidenko and S.C.Mukhopadhyay, “Defect analysis of grit-blasted or spray printed surface using vision sensing technique”, Proceedings of Image and Vision Computing NZ, Nov. 26-28, 2003, Palmerston North, pp. 18-23. Search in Google Scholar

O. Yakhnenko and V. Honavar, “Multi-Instance multi-label learning for image classification with large vocabularies”, BMVC, 2011, pp.1-12.10.5244/C.25.59 Search in Google Scholar

G. Sen Gupta, S.C. Mukhopadhyay and M Finnie, Wi-Fi Based Control of a Robotic Arm with Remote Vision, Proceedings of 2009 IEEE I2MTC Conference, Singapore, May 5-7, 2009, pp. 557-562.10.1109/IMTC.2009.5168512 Search in Google Scholar

F. Cheng, J. Yu, H. Xiong, “Facial expression detection in JAFFE dataset based on Gaussian process classification”, IEEE Transactions on Neural Networks, vol.10, No.21, 2010, pp.16851690.10.1109/TNN.2010.206417620729164Search in Google Scholar

eISSN:
1178-5608
Idioma:
Inglés
Calendario de la edición:
Volume Open
Temas de la revista:
Engineering, Introductions and Overviews, other