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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 9854050, 9 pages
https://doi.org/10.1155/2017/9854050
Research Article

Facial Expression Recognition Using Stationary Wavelet Transform Features

1Department of Computer Engineering, University of Engineering and Technology, Taxila, Taxila 47050, Pakistan
2Department of Software Engineering, University of Engineering and Technology, Taxila, Taxila 47050, Pakistan
3Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22010, Pakistan

Correspondence should be addressed to Muhammad Majid

Received 9 October 2016; Revised 11 December 2016; Accepted 18 December 2016; Published 11 January 2017

Academic Editor: Simone Bianco

Copyright © 2017 Huma Qayyum et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Deepak Kumar Jain, Zhang Zhang, and Kaiqi Huang, “Multi angle optimal pattern-based deep learning for automatic facial expression recognition,” Pattern Recognition Letters, 2017. View at Publisher · View at Google Scholar
  • Nikunja Bihari Kar, Korra Sathya Babu, Arun Kumar Sangaiah, and Sambit Bakshi, “Face expression recognition system based on ripplet transform type II and least square SVM,” Multimedia Tools and Applications, 2017. View at Publisher · View at Google Scholar
  • Xin Jin, Qian Jiang, Shaowen Yao, Dongming Zhou, Rencan Nie, Shin-Jye Lee, and Kangjian He, “Infrared and Visual Image Fusion Method Based on Discrete Cosine Transform and Local Spatial Frequency in Discrete Stationary Wavelet Transform Domain,” Infrared Physics & Technology, 2017. View at Publisher · View at Google Scholar
  • Ghulam Muhammad, Mansour Alsulaiman, Syed Umar Amin, Ahmed Ghoneim, and Mohammed F. Alhamid, “A Facial-Expression Monitoring System for Improved Healthcare in Smart Cities,” IEEE Access, vol. 5, pp. 10871–10881, 2017. View at Publisher · View at Google Scholar
  • Samet Aymaz, and Cemal Köse, “A Novel Image Decomposition-Based Hybrid Technique with Super-Resolution Method for Multi-Focus Image Fusion,” Information Fusion, 2018. View at Publisher · View at Google Scholar