Recognizing Facial Expressions by Using New Algorithm Based on Combined Approach of PSO and MLBP
[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.
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.
RBF Neural Network, Local Binary Patterns, Feature Selection, Algorithm of Particle Swarm Optimization
[1]
H. Kobayashi and F. Hara, ―Recognition of Six Basic Facial Expressions and Their Stength by Neural Network,‖ Proc. of ROMAN, vol. 92, pp. 381-386, 1992.
[2]
Lu J., Plataniotis K. and Venetsanopoulos A., “Face Recognition Using LDA-Based Algorithms”, IEEE Transaction on Neural Networks, Vol. 14, No. 1, pp. 195-200, 2003.
[3]
P. S. Aleksic and A. K. Katsaggelos, ―Automatic Facial Expression Recognition Using Facial Animation Parameters and MultiStream HMMs,‖ IEEE Trans. Information Forensics and Security, vol. 1, no. 1, pp. 3-11, 2006.
[4]
M. Kaur, R. Vashisht, and N. Neeru, ―Recognition of facial expressions with principal component analysis and singular value decomposition,‖ International Journal of Computer Applications, vol. 9, no. 12, pp. 36-40, 2010.
[5]
R. Verma and M.-Y. Dabbagh, ―PERFORMANCE COMPARISONS OF FACIAL EXPRESSION RECOGNITION IN JAFFE DATABASE,‖ International Journal of Pattern Recognition and Artificial Intelligence, vol. 22, no. 3, pp. 445-459, 2008.
[6]
Y. Wang and W. Gong, ―Facial Expression Recognition Based on Local Sensitive Feature Extraction with Gabor Wavelet and LE + LDA,‖ Computational Information Systems, vol. 7, no. 8, pp. 2745-2752, 2011.
[7]
M. Bartlett, G. Littlewort, I. Fasel, and R. Movellan, ―REal Time Face Detection and Facial Expression Recognition: Development and Application to Human Computer Interaction,‖ Proc. CVPR Workshop on computer Vision and Recognition for Human-Computer Interaction, vol. 15, 2003.
[8]
Scholkopf, B., Smola, A.J., Muller K.R., "Nonlinear component analysis as a kernel eigenvalue problem", Neural Computation, Vol. 10, No. 5, pp.1299-1319, 1998.
[9]
S. Berretti and B. Amor, ―Person independent 3D facial expression recognition by a selected ensemble of SIFT descriptors,‖ in Eurographics Workshop on 3D Object Retrieval, 2010, p. 2312.
[10]
Ahonen, T., Pietikainen, M., Hadid,A., Maneppa,T., "Face Recognition Based on the Appearance of Local Regions", International Conference on Pattern Recognition. Pp.153-156, 2004.
[11]
Liao,S., Zhu,X., Lei, Z., Zhang,L., Li, S., "Learning multi-scale block local binary patterns for face recognition", International Conference on Biometrics, pp. 828-837, 2007.
[12]
Ojala, T., Pietikainen, Harwood,M., "A comparative study of texture measures with classification based on feature distributions", Pattern Recognition, Vol. 29, No. 1, pp.51–59, 1996.
[13]
Zhang,G., Huang,Z., Li,Z., "Boosting local binary pattern (LBP)-based face recognition", Proceedings of theSinoBiometrics, vol. 3338, pp. 179- 186, 2004.
[14]
S. Chew, P. Lucey, and S. Lucey, ―Person-independent facial expression detection using constrained local models,‖ Gesture Recognition, pp. 915-920, Mar. 2011.
[15]
J. Zhou, T. Xu, and J. Gan, ―Feature Extraction based on Local Directional Pattern with SVM Decision-level Fusion for Facial Expression Recognition,‖ International Journal of Bio-Science and Bio-Technology, vol. 5, no. 2, pp. 101-110, 2013.
[16]
L. A. Jeni, D. Takacs, and A. Lorincz, ―High Quality Facial Expression Recognition in Video Streams using Shape Related Information only,‖ in Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, 2011, pp. 2168 - 2174.
[17]
S. Yang, S. Member, and B. Bhanu, ―Understanding Discrete Facial Expressions in Video Using an Emotion Avatar Image,‖ IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, vol. 42, no. 4, pp. 980-992, 2012.
[18]
Ojala, T., Pietik¨ainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29 (1996) 51–59.
[19]
T. Ojala, M. Pietikäinen, and T. Mäenpää, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002.
[20]
http://www.ee.oulu.fi/mvg/page/lbp_bibliography.
[21]
Chan, C.H., “Multi-scale local binary pattern histogram for face recognition”, Centre for Vision, Speech and Signal Processing School of Electronics and Physical Sciences University of Surrey Guildford, Surrey, U.K, Submitted for the Degree of Doctor of Philosophy from the University of Surrey, September 2008.
[22]
Chan, C.H.” Multi-scale Local Binary Pattern Histogram for Face Recognition”, Ph.D. Thesis, University of Surrey, Seattle, U.K, 2008.
[23]
Jensen, R., “Combining rough and fuzzy sets for feature selection”, Ph.D. Thesis, University of Edinburgh, 2005.
[24]
J. Kennedy, and R. C. Eberhart, "Particle swarm optimization," Proceedings of IEEE International Conference on Neural Networks, Vol. 4, pp. 1942-1948 1995.
[25]
K. E. parsopoulos, and M. N. Vrahatis, "Recent approaches to global optimization problems through particle swarm optimization," Natural Computing, Vol. 1, pp. 235-306, 2002.
[26]
J. Kennedy, and R. C. Eberhart, "A Discrete Binary version of the particle swarm algorithm," IEEE International Conference on Computational Cybernetics and Simulation, Vol. 5, pp. 4104-4108, 1997.
[27]
P. Lucey et al., ―The Extended Cohn-Kanade Dataset (CK +): A complete dataset for action unit and emotion-specified expression, ‖ no. July, 2010.
[28]
J. Moody and C. Darken, "Learning with Localized receptive fields" in proc, 1988 Connectionist Models ummer School. San Matco. CA:Morgan-Kaufmann 1988.
