Authors:
R. Santhoshkumar,M. Kalaiselvi Geetha,DOI NO:
https://doi.org/10.26782/jmcms.2019.06.00015Keywords:
Emotion Recognition,Non-verbal communication,Body Movement,Human Computer Interaction (HCI),Deep Convolutional Neural Networks (DCNN),BAIVfeature,Abstract
Analysis of human body movements for emotion prediction is necessary for social communication. Body movements, gestures, eye movements and facial expression are some non-verbal communication method used in many applications. Among them emotion prediction from body movements is commonly used because it convey the emotional states of person from different camera view. In this paper, human emotional states predict from full body movements using feed forward deep convolution neural network architecture and Block Average Intensity Value BAIV feature. Both model can be evaluated by emotion action dataset (University of YORK) with 15 types of emotions. The experimental result showed the better recognition accuracy of the feed forward deep convolution neural network architecture.Refference:
I.A.Krizhevsky, I. Sutskever, and G. E. Hinton, (2014),“Imagenet Classification With Deep Convolutional Neural Networks,” In Advances in neural information processing systems, pp. 1097–1105.
II.D.Tran, L. Bourdev, R. Fergus, L.Torresani and M. Paluri, (2015), “Learning Spatiotemporal features with 3d Convolutional networks”,IEEE International Conference on Computer Vision (ICCV), pp. 4489-4497.
III.Damel Rucha, Gurjar Aditya, Joshi Anuja, Nagre Kartik, (2015), “Human Body Skeleton detection and Tracking”, International Journal of Technical Research and Applications, Volume 3, Issue 6, pp.222-225.
IV.Daniel Holden, Jun Saito, Taku Komura. (2016) “A Deep Learning Framework for Character Motion Synthesis and Editing” SIGGRAPH ’16 Technical Paper, July 24 -28, Anaheim, CA, ISBN: 978-1-4503-4279-7/16/07.
V.Enrique Correa, Arnoud Jonker, Michael Ozo, Rob Stolk. (2016) “Emotion Recognition using Deep Convolutional Neural Networks”
VI.F. Zhu and L. Shao, (2014),“Weakly-Supervised Cross-Domain Dictionary Learning for Visual Recognition,” International Journal of Computer Vision, Vol. 109, No. 1-2, pp. 42–59.
VII.F. Zhu and L. Shao, (2015),“Correspondence-Free Dictionary Learning for Cross-View Action Recognition,” In ICPR, pp. 4525–4530.
VIII.F. Zhu, L. Shao, J. Xie, and Y. Fang, (2016),“From Handcrafted to Learned Representations for Human Action Recognition: A Survey,” Image and Vision Computing.
IX.Fatemeh Noroozi, Ciprian Adrian Corneanu, Dorota Kami ́nska, Tomasz Sapi ́ nski, Sergio Escalera, and Gholamreza Anbarjafari (2015) “Survey on Emotional Body Gesture Recognition” Journal of IEEE Transactions on Affective Computing.
X.Gavrilescu, M., (2015) “Recognizing emotions from videos by studying facial expressions, body postures and hand gestures”, 23rdTelecommunication fourm TELFOR,pp. 720-723.
XI.H.Wang, C. Yuan,W. Hu, and C. Sun,(2012), “Supervised Class-Specific Dictionary Learning for Sparse Modeling in Action Recognition,” Pattern Recognition, Vol. 45, No. 11,pp. 3902–3911.
XII.Hatice Gunes, Caifeng Shan, Shizhi Chen, YingLi Tian. (2015) “Bodily Expression for Automatic Affect Recognition. Emotion Recognition: A Pattern Analysis Approach” Published by John Wiley & Sons, Inc.
XIII.Hazel Rose Markus, Shinobu Kitayama.(1991) “Culture and the self: Implementations for cognition, emotion, and motivation” Psychological Review,pp. 224-253.
XIV.Heike Brock. (2018) “Deep learning -Accelerating Next Generation Performance Analysis Systems” 12th Conference of the International Sports Engineering Association, Brisbane, Queensland, Australia, pp. 26–29
.XV.Hiranmayi Ranganathan, Shayok Chakraborty, Sethuraman Panchanathan.(2017) “Multimodal Emotion Recognition using Deep Learning Architectures” http://emofbvp.org/
XVI.J. Arunnehru, M. Kalaiselvi Geetha. (2017) “Automatic Human Emotion Recognition in Surveillance Video” Intelligent Techniques in Signal Processing for Multimedia Security, Springer-Verlag,pp. 321-342.
XVII.Lei Zhang, Shuai Wang, Bing Liu. (2018) “Deep Learningfor Sentiment Analysis: A Survey” https://arxiv.org/pdf/1801.07883.XVIII.Nourhan E, Pablo B, Parisi, Stefan Wermter, (2017),”Emotion recognition from body expressions with Neural Network Architecture”, Algorithm and Learning, HAI 2017, pp. 143-149.
XIX.Pablo Barros, Doreen Jirak, Cornelius Weber, Stefan Wermter. (2015) “Multimodal emotional state recognition using sequence-dependent deep hierarchical features” Neural Networks. 72, pp. 140–151.
XX.Pooya Khorrami, Tom Le Paine, Kevin Brady, Charlie Dagli, Thomas S. Huang. (2017) “How Deep Neural Networks Can Improve Emotion Recognition on Video Data” https://arxiv.org/pdf/1602.07377.pdf.
XXI.Prinzie, A., Van den Poel, D., (2012), Random Forests for multiclass classification: Random MultiNomial Logit. Expert Systems with Applications. Vol.34, 3, pp.1721–1732.
XXII.Samira Ebrahimi Kahou, Vincent Michalski, Kishore Konda, Roland Memisevic, Christopher Pal. (2015) “Recurrent Neural Networks for Emotion Recognition in Video” ICMI 2015, Seattle, WA, USA.
XXIII.Shirbhate Neha, Talele Kiran, (2016), “Human Body Language Understanding for Action detection using Geometric Features”,2ndInternational Conference on Contemporary Computing and Informatics, IEEE, pp.603-607.
XXIV.T. Guha and R. K.Ward, (2012),“Learning Sparse Representations for Human Action Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 34, No. 8, pp. 1576–1588.
XXV.Y. Du,W.Wang, and L.Wang, (2015),“Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110–1118.
XXVI.Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel,(1989), “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural computation,Vol. 1, No. 4, pp. 541–551.
XXVII.Yann LeCun, Yoshua Bengio, Geoffrey Hinton.(2015) “Deep learning” Nature, Vol. 521, pp. 436-444.
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