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Recognition of Facial Expression Based on Local Binary Patterns (LBP)

Olaniyi Abiodun Ayeni*

Department of Cyber Security, School of Computing, Federal University of Technology, Akure, Nigeria.

Periodicity:September - November'2019
DOI : https://doi.org/10.26634/jcom.7.3.16861

Abstract

Face detection and Expression recognition are few the ongoing research areas in computer vision. Most of the time, face detection precedes facial expression recognition, i.e., the result of face detection is fed as input into expression recognition. This paper presents face detection based on Viola and Jones algorithm and the issues relating to expression recognition. A facial expression recognition system based on Local Binary Pattern (LBP) for feature extraction and Support Vector Machine (SVM) for classification is presented. Different Facial expressions of the staff and students of the Federal University of Technology, Akure (FUTA) were captured and used as training samples. Matrix laboratory 2016a was used for implementation. The designed system achieved an overall 95.4% recognition rate, which is improvement over an existing systems.

Keywords

Face Detection, Expression Recognition, Local Binary Pattern, Machine Learning, Emotions, Feature Extraction, Facial Images.

How to Cite this Article?

Abiodun, A. O. (2019). Recognition of Facial Expression Based on Local Binary Patterns (LBP), i-manager's Journal on Computer Science, 7(3),14-22. https://doi.org/10.26634/jcom.7.3.16861

References

[1]. Agrawal, N., Cosgriff, R., & Mudur, R. (2009). Mood detection: Implementing a facial expression recognition system. CS229 Project. Retrieved from http:// citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.374. 8073&r ep=rep1&type=pdf

[2]. Huang,H-F.,Tai,S-C. (2012). Facial expression recognition using near feature extraction algorithm. Electronic Letters on Computer Vision and Image Analysis, 11(1), 41-54.

[3]. Ismaila, W. O., Adetunji, A. B., Falohun, A. S., &Iwashokun, G. B. (2012). A study of features extraction algorithms for human face recognition. Transnational Journal of Science and Technology, 2(6),14-22.

[4]. Khan, R. A., Meyer, A., Konik, H., & Bouakaz, S. (2012, June). Exploring human visual system: Study to aid the development of automatic facial expression recognition framework. In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 49-54). IEEE. https://doi.org/10.1109/CVPRW.2012. 6239186

[5]. Ojala, T., & Pietikäinen, M. (1999). Unsupervised texture segmentation using feature distributions. Pattern Recognition, 32(3), 477-486.

[6]. Rahim, M. A., Azam, M. S., Hossain, N., & Islam, M. R. (2013). Face recognition using Local Binary Patterns (LBP). Global Journal of Computer Science and Technology,13(4), 1-7.

[7]. Riaz, Z., Mayer, C., Wimmer, M., Beetz, M., & Radig, B. (2009, June). A model based approach for expressions invariant face recognition. In International Conference on Biometrics (pp. 289-298). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01793-3_30

[8]. Saaidia, M., Gattal, A., Maamri, M., & Ramdani, M.(2012). Face expression recognition using auto regressive models to train neural network classifiers. In International Journal of New Computer Architectures and their Applications (IJNCAA), 2(3),481-487.

[9]. Shan, C., Gong, S., & McOwan, P. W. (2009). Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27(6), 803-816. https://doi.org 10.1016 /j.imavis. 2008 .08 .005

[10]. Starostenko, O., Contreras, R., Aquino, V. A., Pulido,L. F., Asomoza, J. R., Sergiyenko, O., & Tyrsa, V. (2011). A fuzzy reasoning model for recognition of facial expressions. Computación y Sistemas, 15(2), 163-180.

[11]. Tian, Y. L. (2004, June). Evaluation of face resolution for expression analysis. In 2004 Conference on Computer Vision and Pattern Recognition Workshop (pp. 82-82). IEEE. https://doi.org/10.1109/CVPR.2004.334

[12]. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), (vol. 1, PP.1-9). https:// doi.org/ 10.1109/CVPR.2001.990517

[13]. Way, M. J., Scargle, J. D., Ali, K. M., & Srivastava, A. N. (2012). Advances in machine learning and data mining for astronomy. Chapman and Hall/CRC. https://doi.org/ 10.1201/b11822

[14]. Whitehill, J., & Omlin, C. W. (2006, April). Haar features for facs au recognition. In 7th International Conference on Automatic Face and Gesture Recognition (FGR06) (pp. 5- pp). IEEE. https://doi.org/ 10.1109/FGR.2006.61

[15]. Yang, M. H. (2013). Face Detection. Retrieved from https://faculty.ucmerced.edu/mhyang/papers/facedetection- chapter.pdf

[16]. Zhong, L., Liu, Q., Yang, P., Liu, B., Huang, J., & Metaxas, D. N. (2012, June). Learning active facial patches for expression analysis. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 2562- 2569). IEEE. https://doi.org/10.1109/CVPR.2012.6247974

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