Camera-based facial expression recognition system to analyze customer satisfaction level in a restaurant
DOI:
https://doi.org/10.26439/interfases2019.n012.4638Keywords:
Customer satisfaction, facial expression recognition, histogram of oriented gradients, support vector machine, facial landmarksAbstract
The main objective of this research is to develop a system that recognizes customers’ satisfaction or dissatisfaction in a restaurant based on their facial expressions when receiving the service provided by the establishment. The implementation of the system has a series of stages common to the development of a visual computing project, which will begin with data preprocessing to train the classifier to be used in this case: a support vector machine. This data preprocessing uses the histogram of oriented gradients for detecting a face inside an image, so that only the face outline is cut. In this way, facial landmarks of the image are extracted, the probability that each of the basic feelings appears in the facial expression of people is established, and, based on these probabilities, customers’ satisfaction or dissatisfaction is determined. The results show that the system correctly detected most of the images entered for the tests; however, there were some cases in which, despite the fact that customers were satisfied, they showed certain dissatisfaction expressions caused by external factors.
Downloads
References
Acevedo, D., Negri, P., Buemi, M. E., Fernandez, F. G., y Mejail, M. (2017). A simple geome¬tric-based descriptor for facial expression recognition. 12th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 802-808.
Campbell, C., y Ying, Y. (2011). Learning with support vector machines. Synthesis Lectures on Artificial Intelligence and Machine Learning, 5(1), pp. 1-95.
Carmona Suárez, E. J. (2014). Tutorial sobre máquinas de vectores soporte (SVM). Recuperado de http://www.ia.uned.es/~ejcarmona/publicaciones/[2013-Carmona]%20SVM.pdf
Dalal N., y Triggs, B. (2005). Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1, pp. 886-893. doi:10.1109/CVPR.2005.177
Davis, F. (1994). El lenguaje de los gestos. Buenos Aires: Emecé Editores.
Ekman P., y Oster, H. (1979). Expresiones faciales de la emoción. Annual Review of Psychology, 30, pp. 527-554.
Gao, G., Jia, K., y Jiang, B. (2015). An automatic geometric features extracting approach for facial expression recognition based on corner detection. International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 302-305. Adelaide, SA, Australia: IEEE. doi:10.1109/IIH-MSP.2015.33
Khan, S., Rahmani, H., Ali Shah, S. A., y Bennamoun, M. (2018). A guide to convolutional neural networks for computer vision. Morgan & Claypool Publishers.
Lago, P., y Jiménez Guarín, C. (2013). An affective inference model based on facial expression analysis. IEEE Latin America Transactions, 12(3), pp. 423-429.
Ouanan, H., Ouanan, M., y Aksasse, B. (2016). Facial landmark localization: Past, present and future. 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 487-493.
Ryu, B., Rivera, A. R., Kim, J., y Chae, O. (2017). Local directional ternary pattern for facial expression recognition. IEEE Transactions on Image Processing, 26(12), pp. 6006-6018.
Slim, M., Kachouri, R., y Atitallah, A. (2018). Customer satisfaction measuring based on the most significant facial emotion. 15th IEEE International Multi-Conference on Systems, Signals & Devices (SSD), pp. 502-507.
Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., y Movellan, J. R. (2014). The faces of engagement: Automatic recognition of student engagementfrom facial expressions. IEEE Transactions on Affective Computing, 5(1), pp. 86-98.
Xia, L. (2014). Facial expression recognition based on SVM. 7th IEEE International Conference on Intelligent Computation Technology and Automation, pp. 256-259.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under an Attribution 4.0 International (CC BY 4.0) License. that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Last updated 03/05/21
