Rough Decision Making of Facial Expression Detection

Authors

DOI:

https://doi.org/10.26713/cma.v15i1.2598

Keywords:

Rough set theory, Decision making problems, Facial expression detection

Abstract

This paper introduces a novel framework named rough decision making which extends the principles of rough set theory. The mathematical model proposed consists of a fuzzy intervalvalued knowledge-based information system that includes a non-empty finite universe of objects, a derived interval-valued fuzzy set of attributes, and a fuzzy interval-valued set of decisions. To manage complexity, the roughness of decision-making is introduced by defining lower and upper rough decisions for each object. Further, this model is implemented on a real-time affective image database RAF-DB for facial expression detection. This approach is found to provide a comprehensive analysis of facial expressions, demonstrating effective classification even in the presence of uncertainty.

Downloads

Download data is not yet available.

References

F. Chacón-Gómez, E. Cornejo and J. Medina, Decision making in fuzzy rough set theory, Mathematics 11(19) (2023), 4187, URL: 10.3390/math11194187.

P. Chen, G. Wang, Y. Yang and J. Zhou, Facial expression recognition based on rough set theory and SVM, in: Rough Sets and Knowledge Technology, G.-Y. Wang, J. F. Peters, A. Skowron and Y. Yao (editors), 772 – 777 (2006), Springer, Berlin — Heidelberg, DOI: 10.1007/11795131_112.

S. Li and W. Deng, Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition, IEEE Transactions on Image Processing 28(1) (2019), 356 – 370, DOI: 10.1109/TIP.2018.2868382.

H. Li and X. Zhou, Risk decision making based on decision-theoretic rough set: a three-way view decision model, International Journal of Computational Intelligence Systems, 4(1) (2011), 1 – 11, DOI: 10.1080/18756891.2011.9727759.

L. Liao, Y. Zhu, B. Zheng, X. Jiang and J. Lin, FERGCN: Facial Expression Recognition Based on Graph Convolution Network, Machine Vision and Applications 33 (2022), Article number 40, DOI: 10.1007/s00138-022-01288-9.

J. L. Ngwe, K. M. Lim, C. P. Lee and T. S. Ong, PAtt-Lite: Lightweight patch and attention mobilenet for challenging facial expression recognition, IEEE Access 12 (2024), 79327 – 79341, DOI: 10.1109/ACCESS.2024.3407108.

E. Parcham, N. Mandami, A.N. Washington and H.R. Arabnia, Facial Expression recognition based on fuzzy networks, 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2016, pp. 829 – 835, DOI: 10.1109/CSCI.2016.0161.

Z. Pawlak, Rough sets, International Journal of Computer & Information Sciences 11(5) (1982), 341 – 356, DOI: 10.1007/BF01001956.

B. Praba, S. Pooja and N. Sivakumar, Attribute based double bounded rough neutrosophic sets in facial expression detection, Neutrosophic Sets and Systems 49 (2022), 324 – 340.

R. Slowinski, S. Greco and B. Matarazzo, Rough sets in decision making, in: Encyclopedia of Complexity and Systems Science, R. A. Meyers (editor), 7753 – 7787, Springer, New York (2009), DOI: 10.1007/978-0-387-30440-3_460.

T. Tuncer, S. Dogan, M. Abdar, M. E. Basiri and P. Pławiak, Face recognition with triangular fuzzy set-based local cross patterns in wavelet domain, Symmetry 11(6) (2019), 787, DOI: 10.3390/sym11060787.

Z. Zhang, A rough set approach to intuitionistic fuzzy soft set based decision making, Applied Mathematical Modelling 36 (10) (2012), 4605 – 4633, DOI: 10.1016/j.apm.2011.11.071.

Downloads

Published

24-04-2024
CITATION

How to Cite

Praba, B., Charumathi, P., & Anand, B. (2024). Rough Decision Making of Facial Expression Detection. Communications in Mathematics and Applications, 15(1), 397 – 405. https://doi.org/10.26713/cma.v15i1.2598

Issue

Vol. 15 No. 1 (2024)

Section

Research Article

License

Creative Commons Licence 4.0 by  

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 a CCAL 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.