Analysis of Customer Emotion from Video based Feedback of a Product

Authors(4) :-Bharathi E, Bagyalakshmi M, Ambika G, Priyanka G

Due to the high levels of competition in a global market, companies have put more effort on building strong customer relationships and increasing customer satisfaction levels. Now-a-days due to technological improvements in information and communication technologies gives a highly anticipated key contributor to improve the customer experience and satisfaction in service episodes is through the application of video analytics, such as to evaluate the customer’s emotions over the complete service cycle. Currently, emotion recognition from video could be a difficult analysis space. One of the foremost effective solutions to deal with this challenge is to utilize each audio and visual part as two sources contained within the video knowledge to form an overall assessment of the emotion. The combined use of audio and visual knowledge sources presents further challenges, such as determining the optimal data fusion technique prior to classification. In this paper, we propose an audio–visual emotion recognition system to detect the universal six emotions (happy, angry, sad, disgust, surprise, and fear) from video data. The detected customer emotions are then mapped and translated to provide client satisfaction scores. The projected client satisfaction video analytics system will operate over video conferencing or video chat. The effectiveness of our proposal is verified through numerical results.

Authors and Affiliations

Bharathi E
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimabatore, Tamil Nadu, India
Bagyalakshmi M
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimabatore, Tamil Nadu, India
Ambika G
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimabatore, Tamil Nadu, India
Priyanka G
Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimabatore, Tamil Nadu, India

Viola Jones, Local Binary Pattern , Energy Entropy ,K-NN classification.

  1. K. A. Richards and E. Jones, “Customer relationship management: Finding value drivers,” Ind. Market. Manage., vol. 37, pp. 120–130, 2008.
  2. http://www.academia.edu/266817/Online_Multi-Person_Tracking-by-Detection_From_a_Single_Uncalibrated_Camera
  3. L. Vidrascu and L. Devillers, “Detection of real-life emotions in call
  4. centers,” in Proc. Annual Conf. Int. Speech Commun. Assoc., 2005,
  5. pp. 1841–1844.
  6. D. Pappas, I. Androutsopoulos, and H. Papageorgiou, “Anger detection in
  7. call center dialogues,” in Proc. IEEE Int. Conf. Cognitive Infocommun.,
  8. 2015, pp. 139–144.
  9. N. Dalal and B. Triggs, ‘‘Histograms of oriented gradients for human detection,’’ in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), vol. 1. Jun. 2005, pp. 886–893.
  10. J. Berclaz, F. Fleuret, E. Turetken, and P. Fua, ‘‘Multiple object tracking using k-shortest paths optimization,’’ IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 9, pp. 1806–1819, Sep. 2011.
  11. A. A. Butt and R. T. Collins, ‘‘Multi-target tracking by Lagrangian relaxation to min-cost network flow,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 1846–1853.
  12. L. Leal-Taixe, M. Fenzi, A. Kuznetsova, B. Rosenhahn, and S. Savarese, ‘‘Learning an image-based motion context for multiple people tracking,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 3542–3549.
  13. A. Gilmore, L. Moreland, "Call centers: How can service quality be managed?", Irish Market. Rev, vol. 13, no. 1, pp. 3-11, 2000.
  14. A. Feinberg, I. S. Kim, L. Hokama, K. De Ruyter, C. Keen, "Operational determinants of caller satisfaction in the call center", Int. J. Service Ind. Manage., vol. 11, no. 2, pp. 131-141, 2000.
  15. S. Ben-David, A. Roytman, R. Hoory, Z. Sivan, "Using voice servers for speech analytics", Proc. Int. Conf. Digit. Telecommun, Aug. 29–31, 2006.
  16. D. Melamed, M. Gilbert, "Samsa: Speech analytics", Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, pp. 397-416, 2011.
  17. R. Polig et al., "Giving text analytics a boost", IEEE Micro, vol. 34, no. 4, pp. 6-14, Jul./Aug. 2014.
  18. R., Polig, K. Atasu, C. Hagleitner, "Token-based dictionary pattern matching for text analytics", Proc. 23rd Int. Conf. Field Programmable Logic Appl., pp. 1-6, 2013.
  19. Priyanka.G, “Prediction of Airline Delays using K Nearest Neighbor Algorithm”, International Journal of Emerging Technology and Innovation Engineering (IJETIE), ISSN: 2394 – 6598, Vol. 4, No. 5, pp.87-90, 2018.
  20. Priyanka.G,“Data Analytics in Agriculture”, International Journal of Emerging Technology and Innovation Engineering (IJETIE), ISSN: 2394 – 6598, Vol. 4, No. 5, pp. 91-94, 2018.

Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 44-52
Manuscript Number : CSEIT19527
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Bharathi E, Bagyalakshmi M, Ambika G, Priyanka G, "Analysis of Customer Emotion from Video based Feedback of a Product", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.44-52, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT19527
Journal URL : http://ijsrcseit.com/CSEIT19527

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