IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 5 - Issue 3, March 2016 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Facial Expression Recognition Through Machine Learning

[Full Text]

 

AUTHOR(S)

Nazia Perveen, Nazir Ahmad, M. Abdul Qadoos Bilal Khan, Rizwan Khalid, Salman Qadri

 

KEYWORDS

CVIPtools, RST- Invariant, KNN, Human-Machine Interfaces

 

ABSTRACT

Facial expressions communicate non-verbal cues, which play an important role in interpersonal relations. Automatic recognition of facial expressions can be an important element of normal human-machine interfaces; it might likewise be utilized as a part of behavioral science and in clinical practice. In spite of the fact that people perceive facial expressions for all intents and purposes immediately, solid expression recognition by machine is still a challenge. From the point of view of automatic recognition, a facial expression can be considered to comprise of disfigurements of the facial parts and their spatial relations, or changes in the face's pigmentation. Research into automatic recognition of the facial expressions addresses the issues encompassing the representation and arrangement of static or dynamic qualities of these distortions or face pigmentation. We get results by utilizing the CVIPtools. We have taken train data set of six facial expressions of three persons and for train data set purpose we have total border mask sample 90 and 30% border mask sample for test data set purpose and we use RST- Invariant features and texture features for feature analysis and then classified them by using k- Nearest Neighbor classification algorithm. The maximum accuracy is 90%.

 

REFERENCES

[1] Giorgana, G., and Ploeger, P. G., 2012, Facial expres-sion recognition for domestic service robots, In Robo Cup 2011: Robot Soccer World Cup XV, pp. 353-364.

[2] Wang, Z. and Xiao, N., 2013, Using MD-Adaboost to Enhance Classifier of Facial Expression Recognition, Journal of Computational Information Systems, 9(3), 923- 932.

[3] Ghimire, D., and Lee, J., 2013, Geometric feature-based facial expression recognition in image sequences using multi-class Adaboost and support vector ma-chines, Sensors, 13(6), 7714-7734.

[4] Abidin, Z., and Harjoko, A., 2012, A neural network based facial expression recognition using fisher-face, International Journal of Computer Applications, 59(3), 30-34.

[5] Zavaschi, T. H., Britto, A. S., Oliveira, L. E., and Koerich, A. L., 2013, Fusion of feature sets and classifiers for facial expression recognition, Expert Systems with Applications, 40(2), 646-655.

[6] Sarawagi, V., and Arya, K. V., 2013, Automatic facial expression recognition for image sequences, In Contemporary Computing (IC3), Sixth International Conference on, 278-282.

[7] Hablani, R., Chaudhari, N., and Tanwani, S., 2013, Recognition of Facial Expressions using Local Binary Patterns of Important Facial Parts, International Journal of Image Processing (IJIP), 7(2), 163- 170.

[8] Lingareddy, M. and D, Haritha., 2013, An Optimal Face Recognition Tool, International Journal of Research in Engineering and Technology, 2(3), 351-357.

[9] Zhang, H., Luo, S., and Yoshie, O., 2013, Facial expression recognition by analyzing features of conceptual regions, In Computer and Information Science, 529-534.

[10] Das, D., 2014, Humanís Facial Parts Extraction to Recognize Facial Expression, International journal on information theory, 3(3), 65-72.

[11] Hai, T. S., Thai, L. H. and Thuy, N. T., 2015, Facial Ex-pression Classification Using Artificial Neural Network and K-Nearest Neighbor, International Journal of Information Technology and Computer Science (IJITCS), 7(3), 27- 32.

[12] De, A., Saha, A., and Pal, M. C., 2015, A Human Facial Expression Recognition Model Based on Eigen Face Approach, Procedia Computer Science, 45, 282-289.

[13] Chao, W. L., Ding, J. J., and Liu, J. Z., 2015, Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projec-tion, Signal Processing, 4(7), 1-39.

[14] Yu, H., and Liu, H., 2015), Combining appearance and geometric features for facial expression recognition, In Sixth International Conference on Graphic and Image Processing , 944308-944308.

[15] Zhang, L., and Tjondronegoro, D., 2011,. Facial expression recognition using facial movement features, IEEE Transactions on Affective Computing, 2(4), 219-229.