Deep learning vs. kernel methods: Performance for emotion prediction in videos

2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015

Volumn , Issue , 2015, Pages 77-83

  Baveye, Yoann ^a,b   Dellandrea, Emmanuel ^b   Chamaret, Christel ^a   Chen, Liming ^b  

^a Technicolor 975 ^*  (France)

^b ECOLE CENTRALE DE LYON   (France)

Author keywords

affective computing; benchmarking; continuous emotion prediction; deep learning

Indexed keywords

BENCHMARKING; INTELLIGENT COMPUTING; NEURAL NETWORKS; OBJECT RECOGNITION;

AFFECTIVE COMPUTING; COMPUTATIONAL MODEL; CONVOLUTIONAL NEURAL NETWORK; CREATIVE COMMONS; DEEP LEARNING; EMOTION PREDICTIONS; MOVIE CONTENT ANALYSIS; TRANSFER LEARNING;

FORECASTING;

EID: 84964008020     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACII.2015.7344554     Document Type: Conference Paper

References (27)

1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems 25, 2012, pp. 1097-1105.
2. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," arXiv preprint arXiv:1409.4842, 2014. [Online]. Available: http://arxiv.org/abs/1409.4842
3. Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, "LIRIS-ACCEDE: A video database for affective content analysis," IEEE Transactions on Affective Computing, vol. 6, no. 1, pp. 43-55, Jan 2015.
4. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2009, pp. 248-255.
5. A. Hanjalic and L.-Q. Xu, "Affective video content representation and modeling," IEEE Transactions on Multimedia, vol. 7, no. 1, pp. 143-154, Feb. 2005.
6. S. Zhang, Q. Huang, S. Jiang, W. Gao, and Q. Tian, "Affective visualization and retrieval for music video," IEEE Transactions on Multimedia, vol. 12, no. 6, pp. 510-522, Oct. 2010.
7. N. Malandrakis, A. Potamianos, G. Evangelopoulos, and A. Zlatintsi, "A supervised approach to movie emotion tracking," in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, pp. 2376-2379.
8. H. Drucker, C. J. Burges, L. Kaufman, A. Smola, V. Vapnik et al., "Support vector regression machines," Advances in neural information processing systems, vol. 9, pp. 155-161, 1997.
9. F. Weninger, F. Eyben, B. W. Schuller, M. Mortillaro, and K. R. Scherer, "On the acoustics of emotion in audio: what speech, music, and sound have in common," Frontiers in psychology, vol. 4, pp. 1664-1078, 2013.
10. I. Kanluan, M. Grimm, and K. Kroschel, "Audio-visual emotion recognition using an emotion space concept," in 16th European Signal Processing Conference, Lausanne, Switzerland, 2008.
11. S. Zhang, Q. Tian, Q. Huang, W. Gao, and S. Li, "Utilizing affective analysis for efficient movie browsing," in 16th IEEE International Conference on Image Processing (ICIP), Nov. 2009, pp. 1853-1856.
12. L. Canini, S. Benini, and R. Leonardi, "Affective recommendation of movies based on selected connotative features," IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 4, pp. 636-647, 2013.
13. M. Nicolaou, H. Gunes, and M. Pantic, "Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space," IEEE Transactions on Affective Computing, vol. 2, no. 2, pp. 92-105, April 2011.
14. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, "Backpropagation applied to handwritten zip code recognition," Neural computation, vol. 1, no. 4, pp. 541-551, 1989.
15. M. Matsugu, K. Mori, Y. Mitari, and Y. Kaneda, "Subject independent facial expression recognition with robust face detection using a convolutional neural network," Neural Networks, vol. 16, no. 5, pp. 555-559, 2003.
16. S. E. Kahou, C. Pal, X. Bouthillier, P. Froumenty, c. Gulçehre, R. Memisevic, P. Vincent, A. Courville, Y. Bengio, R. C. Ferrari, M. Mirza, S. Jean, P.-L. Carrier, Y. Dauphin, N. Boulanger-Lewandowski, A. Aggarwal, J. Zumer, P. Lamblin, J.-P. Raymond, G. Desjardins, R. Pascanu, D. Warde-Farley, A. Torabi, A. Sharma, E. Bengio, M. Coté, K. R. Konda, and Z. Wu, "Combining modality specific deep neural networks for emotion recognition in video," in Proceedings of the 15th ACM on International Conference on Multimodal Interaction, ser. ICMI '13, 2013, pp. 543-550.
17. R. Cowie, M. Sawey, C. Doherty, J. Jaimovich, C. Fyans, and P. Stapleton, "Gtrace: General trace program compatible with emotionml," in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), Sept 2013, pp. 709-710.
18. M. M. Bradley and P. J. Lang, "Measuring emotion: the self-assessment manikin and the semantic differential," Journal of behavior therapy and experimental psychiatry, vol. 25, no. 1, pp. 49-59, Mar. 1994.
19. K. R. Scherer, "Appraisal considered as a process of multi-level sequential checking," in Appraisal processes in emotion: Theory, Methods, Research, K. R. Scherer, A. Schorr, and T. Johnstone, Eds. Oxford University Press, 2001, pp. 92-120.
20. A. Metallinou and S. Narayanan, "Annotation and processing of continuous emotional attributes: Challenges and opportunities," in 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Apr. 2013, pp. 1-8.
21. S. Mariooryad and C. Busso, "Analysis and compensation of the reaction lag of evaluators in continuous emotional annotations," in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), Sept 2013, pp. 85-90.
22. M. Nicolaou, H. Gunes, and M. Pantic, "Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space," IEEE Transactions on Affective Computing, vol. 2, no. 2, pp. 92-105, April 2011.
23. M. Soleymani, M. N. Caro, E. M. Schmidt, C.-Y. Sha, and Y.-H. Yang, "1000 songs for emotional analysis of music," in Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia. ACM, 2013, pp. 1-6.
24. J. J. Randolph, "Free-marginal multirater kappa (multirater -free): An alternative to fleiss fixed-marginal multirater kappa," Paper presented at the Joensuu University Learning and Instruction Symposium, Oct. 2005.
25. J. R. Landis and G. G. Koch, "The measurement of observer agreement for categorical data," Biometrics, vol. 33, no. 1, pp. 159-174, Mar. 1977.
26. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: Convolutional architecture for fast feature embedding," arXiv preprint arXiv:1408.5093, 2014.
27. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.