Abstract
Scene understanding targets on the automatic identification of thoughts, opinion, emotions, and sentiment of the scene with polarity. The sole aim of scene understanding is to build a system which infer and understand the image or a video just like how humans do. In the paper, we propose two algorithms- Eigenfaces and Bezier Curve based algorithms for scene understanding in images. The work focuses on a group of people and thus, targets to perceive the sentiment of the group. The proposed algorithm consist of three different phases. In the first phase, face detection is performed. In the second phase, sentiment of each person in the image is identified and are combined to identify the overall sentiment in the third phase. Experimental results show Bezier curve approach gives better performance than Eigenfaces approach in recognizing the sentiments in multiple faces.
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References
Ijaz Khan, Hadi Abdullah and Mohd Shamian Bin Zainal (2013) Efficient eyes and mouth detection algorithm using combination of Viola Jones and Skin color pixel detection, International Journal of Engineering and Applied Sciences.
J. Zhao and G. Kearney (1996) Classifying facial emotions by back propagation neural networks with fuzzy inputs. International Conference on Neural Information Processing,1:454–457
Jianbo Yuan, Quanzeng You, Sean Mcdonough, Jiebo Luo (2013), Sentribute: Image Sentiment Analysis from a Mid-level Perspective, Association for Computing Machinery, doi:10.1145/2502069.2502079
KaalaiSelvi R, Kavitha P, Shunmuganathan K (2014), Automatic Emotion Recognition in video, International Conference on Green Computing Communication and Electrical Engineering, p. 1–5, doi:10.1109/ICGCCEE.2014.6921398
Li-Jia Li, Richard Socher, Li Fei-Fei (2009), Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework, Computer Vision and Pattern Recognition(CVPR),http://vision.stanford.edu/projects/totalscene/
Luo, R., Lin, P., Chang, L. (2012) Confidence fusion based emotion recognition of multiple persons for human-robot interaction. International Conference on Intelligent Robots and Systems (ICIRS), p 4590–4595
M. A. Turk, A. P. Pentland (1991), Face recognition using eigenfaces, Computer Vision and Pattern Recognition, IEEE Computer Society Conference
Matthew Shreve, Jesse Brizzi, Sergiy Fefilatyev, Timur Luguev, Dmitry Goldgof and Sudeep Sarkar (2014) Automatic expression spotting in videos. Image and Vision Computing, Elseiver
Michael Lyons, Miyuki Kamachi, and Jiro Gyoba, Facial Expression Database: Japanese Female Facial Expression Database, http://www.kasrl.org/jaffe.html
Navleen Kaur, Madhu Bahl (2014) Emotion Extraction in Color Images using Hybrid Gaussian and Bezier Curve Approach, International Journal of Application or Innovation in Engineering and Management. Available via http://www.ijaiem.org/Volume3Issue9/IJAIEM-2014-09-25-60.pdf
Perez Rosas, Veronica, Rada Mihalcea, Louis-Philippe Morency (2013) Multimodal Sentiment Analysis Of Spanish Online Videos”, IEEE Intelligent Systems,28(3):38–45
R. Karthika, Parameswaran Latha, B.K., P., and L.P., S. (2016) Study of Gabor wavelet for face recognition invariant to pose and orientation. Proceedings of the International Conference on Soft Computing Systems, Advances in Intelligent Systems and Computing, 397:501–509
S. L. Nair, Manjusha R and Parameswaran latha (2015) A survey on context based image annotation techniques. International Journal of Applied Engineering Research,10:29845–29856
Sarfraz, M., M. R. Asim, A. Masood (2010), Capturing outlines using cubic Bezier curves, Information and Communication Technologies: From Theory to Applications.539–540,doi:10.1109/ICTTA.2004.1307870
Thuseethan, Kuhanesan S (2014) Eigenface Based Recognition of Emotion Variant Faces. Computer Engineering and Intelligent Systems,5(7)
Xiao J., Russell, B. C., Hays J., Ehinger, K. A., Oliva, A., Torralba (2013), Basic Level Scene Understanding: from labels to structure and beyond, doi:10.1145/2407746.2407782
Yong-Hwan Lee, Woori Han, Youngseop Kim (2013) Emotion Recognition from Facial Expression Analysis using Bezier Curve Fitting, 16th International Conference on Network Based Information Systems,doi: 10.1109/NBiS.2013.39
Yu-Gang Jiang, Baohan Xu, Xiangyang Xue (2014), Predicting Emotions in User Generated Videos, Association for the Advancement of Artificial Intelligence, Canada, July
Z. Hammal, A. Caplier (2004) Eyes and eyebrows parametric models for automatic segmentation, 6th IEEE Southwest Symposium on Image Analysis and Interpretation, p 138–141.
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Athira, S., Manjusha, R., Parameswaran, L. (2016). Scene Understanding in Images. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_20
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DOI: https://doi.org/10.1007/978-3-319-47952-1_20
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