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Recognizing Facial Expressions by Using New Algorithm Based on Combined Approach of PSO and MLBP
Current Issue
Volume 6, 2019
Issue 1 (January)
Pages: 10-15   |   Vol. 6, No. 1, January 2019   |   Follow on         
Paper in PDF Downloads: 28   Since May 4, 2019 Views: 850   Since May 4, 2019
Authors
[1]
Narges Shafieian, Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran.
[2]
Ali Amiri, Department of Computer Engineering, Zanjan University, Zanjan, Iran.
Abstract
Because of the relationship between man and computer in the virtual world and build a so-called emotional relationship between them, facial expression recognition in recent years has been highly regarded. Six main mode of face that often considered in order to recognition are includes: happiness, sadness, anger, surprise, fear and hatred. In this paper a new method is presented for detecting the main mode of face based on facial images feature extraction by using the combined approach of the structure of multi-scale local binary patterns (MLBP) and algorithm of particle swarm optimization (PSO). In the proposed method, first, the facial images divided into three parts and then the features of MLBP are extracted from two part of mouth and eyes in main images, then the best subset of features found by PSO algorithm, and a feature vector is formed for each of the facial modes. By using obtained feature vector, a RBF neural network is trained and for testing have been used of faces from that have not been used for training. Obtained results of tests on a cohn-kanade’s public database showed that the proposed method compared with the previous work that authors had done on the fixed images, gaining better recognition accuracy.
Keywords
RBF Neural Network, Local Binary Patterns, Feature Selection, Algorithm of Particle Swarm Optimization
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