Classification of Facial Expressions Using K-Nearest Neighbor Classifier
In this paper, we have presented a fully automatic technique for detection and classification of the six basic facial expressions from nearly frontal face images. Facial expressions are communicated by subtle changes in one or more discrete features such as tightening the lips, raising the eyebrows, opening and closing of eyes or certain combinations of them. These discrete features can be identified through monitoring the changes in muscles movement (Action Units) located near about the regions of mouth, eyes and eyebrows. In this work, we have used eleven feature points that represent and identify the principle muscle actions as well as provide measurements of the discrete features responsible for each of the six basic human emotions. A multi-detector approach of facial feature point localization has been utilized for identifying these points of interests from the contours of facial components such as eyes, eyebrows and mouth. Feature vector composed of eleven features is then obtained by calculating the degree of displacement of these eleven feature points from a non-changeable rigid point. Finally, the obtained feature sets are used for training a K-Nearest Neighbor Classifier so that it can classify facial expressions when given to it in the form of a feature set. The developed Automatic Facial Expression Classifier has been tested on a publicly available facial expression database and on an average 90.76% successful classification rate has been achieved.
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- 2.Mehrabian, A.: Communication without Words. Psychology Today 2(4), 53–56 (1968)Google Scholar
- 6.Chibelushi, C.C., Bourel, F.: Facial Expression Recognition: A Brief Tutorial Overview. Available Online at: http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/CHIBELUSHI1/CCC_FB_FacExprRecCVonline.pdf”
- 7.Ekman, P., Friesen, W.V.: Unmasking the Face. Prentice Hall, Englewood Cliffs (1975)Google Scholar
- 8.Cottrell, G.W., Metcalfe, J.: EMPATH: Face, Emotion, Gender Recognition Using Holons. In: Advances in Neural Information Processing Systems, vol. 3, pp. 564–571 (1991)Google Scholar
- 9.Rahardja, A., Sowmya, A., Wilson, W.H.: A Neural Network Approach to Component Versus Holistic Recognition of Facial Expression in Images. Intelligent Robots and Computer Vision X: Algorithms and Techniques 1607, 62–70 (1991)Google Scholar
- 10.Kobayashi, H., Hara, F.: Recognition of Mixed Facial Expressions and Their Strength by a Neural Network. In: IEEE International Conference on Acoustic, Speech and Signal Processing, pp. 1495–1498. IEEE Computer Society Press, Los Alamitos (1992)Google Scholar
- 11.Vanger, P., Honlinger, R., Haken, H.: Applications of Synergetics in Decoding Facial Expression of Emotion. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 24–29. IEEE Computer Society Press, Los Alamitos (1995)Google Scholar
- 14.Pantic, M., Rothkrantz, L.J.M.: An Expert System for Multiple Emotional Classification of Facial Expressions. In: 11th IEEE International Conference on Tools with Artificial Intelligence, pp. 113–120. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
- 15.Feng, X., Pietikainen, M., Hadid, A.: Facial Expression Recognition with Local Binary Patterns and Linear Programming. IEEE Trans. Pattern Recognition and Image Analysis 15(2), 546–548 (2005)Google Scholar
- 16.Sohail, A.S.M., Bhattacharya, P.: Localization of Facial Feature Regions Using Anthropometric Face Model. In: First International Conference on Multidisciplinary Information Sciences and Technologies (2006)Google Scholar
- 18.Efford, N.: Digital Image Processing: A Practical Introduction Using Java. Addison-Wesley, Essex (2000)Google Scholar
- 21.Dasarathy, B.V. (ed.): Nearest Neighbor: Pattern Classification Techniques (Nn Norms: Nn Pattern Classification Techniques). IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
- 23.Dudani, S.A.: The Distance-weighted K-Nearest-Neighbor Rule. IEEE Transaction on Systems, Man and Cybernetics 6, 325–327 (1976)Google Scholar