Luminance Sticker Based Facial Expression Recognition Using Discrete Wavelet Transform for Physically Disabled Persons
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
Developing tools to assist physically disabled and immobilized people through facial expression is a challenging area of research and has attracted many researchers recently. In this paper, luminance stickers based facial expression recognition is proposed. Recognition of facial expression is carried out by employing Discrete Wavelet Transform (DWT) as a feature extraction method. Different wavelet families with their different orders (db1 to db20, Coif1 to Coif 5 and Sym2 to Sym8) are utilized to investigate their performance in recognizing facial expression and to evaluate their computational time. Standard deviation is computed for the coefficients of first level of wavelet decomposition for every order of wavelet family. This standard deviation is used to form a set of feature vectors for classification. In this study, conventional validation and cross validation are performed to evaluate the efficiency of the suggested feature vectors. Three different classifiers namely Artificial Neural Network (ANN), k-Nearest Neighborhood (kNN) and Linear Discriminant Analysis (LDA) are used to classify a set of eight facial expressions. The experimental results demonstrate that the proposed method gives very promising classification accuracies.
- Ekman, P., and Friesen, W., Facial action coding system: a technique for the measurement of facial movement. Palo Alto: Consulting Psychologists Press, 1978.
- Yang, P., Liu, Q., and Metaxas, D. N., Boosting coded dynamic features for facial action units and facial expression recognition. IEEE Proceedings on Computer Vision and Pattern Recognition, 1–6, 2007
- Ojala, T., and Pietikainen, M., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24:971–987, 2002. CrossRef
- Pantic, M., and Patras, I., Detecting facial actions and their temporal segments in nearly frontal-view face image sequences. IEEE International Conference on Systems, Man and Cybernetics, Delft University of Technology, New South Wales, Netherlands, 3358–3363, 2005.
- Kanade, T., Cohn, J. F., and Tian, Y., Comprehensive database for facial expression analysis. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 46–53, 2000.
- Pantic, M., Valstar, M., Rademaker, R., and Maat, L., Web-based database for facial expression analysis. IEEE International Conference on Multimedia and Expo, Delft University of Technology, Netherlands, 317–321, 2005.
- Wang, S., and Xue, J., Case-based facial action units recognition using interactive genetic algorithm. Lecture Notes in Computer Science, Affective Computing and Intelligent Interaction, Springer, Volume 3784/2005, 80–87, 2005.
- Patras, I., and Pantic, M., Particle filtering with likelihoods for tracking facial features. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, 97–102, 2004.
- Lien, J.-J. J., Kanade, T., Cohn, J., and Li, C., Detection, tracking and classification of action units in facial expression. Elsevier Trans. Robot. Auton. Syst. 31(3):131–146, 2000. CrossRef
- Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Comparison of different EEG frequency bands based human emotion classification using discrete wavelet transform. Journal of Cognitive Computation (IJCC), Springer Publication, United Kingdom, 2009.
- Murugappan, M., Nagarajan, R., Yaacob, S., Classification of human emotions using discrete wavelet transform. Journal of Biomedical Science and Engineering, Scientific Research Publication, Hongkong, 2009.
- Garcia, C., Zikos, G., Tziritas, G., A wavelet based framework for face recognition. Proceedings of 5th European Conference on Computer Vision, Workshop on Advances in Facial Image Analysis and Recognition Technology, Freiburg, Germany, 1998.
- Garcia, C., Zikos, G., Tziritas, G., Wavelet packet analysis for face recognition. Image and Vision Computing 18 (Elsevier):289–297, 2000.
- Murugappan, M., Nagarajan, R., Yaacob, S., Comparison of different wavelet features from EEG signals for classifying human emotions. Proceedings of IEEE Symposium and Industrial Electronics and Applications (ISIEA), Kuala Lumpur, Malaysia, 2009.
- Murugappan, M., Rizon, M., Nagarajan, R., and Yaacob, S., FCM clustering of human emotions using wavelet based features from EEG. Trans. Biomed. Soft Comput. Hum. Sci. IJBSCHS 14(2):35–40, 2009.
- Satiyan, M., Nagarajan, R., and Hariharan, M., Recognition of facial expression using Haar wavelet transform. Trans. Int. J. Electr. Electron. Syst. Res. JEESR Univ. Technol. Mara UiTM 3:91–99, 2010.
- Satiyan, M., and Nagarajan, R., Recognition of facial expression using Haar-like feature extraction method. Proceedings of the 3rd IEEE International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, Malaysia, 2010.
- Qualisys—Motion Capture System. http://www.qualisys.com. (retrieved on Sept. 2009).
- Fausett, L., Fundamental of neural network: architectures, algorithms and applications. New Jersey: Prentice-Hall Inc., p. 461, 1994.
- Luger, G. F., and Stubblefield, W. A., Artificial intelligence: structures and strategies for complex problem solving, 3rd edition. Boston: Addison Wesley Longman Inc., 1997.
- Murugappan, M., Rizon, M., Nagarajan, R., and Yaacob, S., An investigation on visual and audiovisual stimulus based emotion recognition using EEG. Int. J. Med. Eng. Infomat. (IJMEI), USA, 1(3):342–356, 2009.
- Luminance Sticker Based Facial Expression Recognition Using Discrete Wavelet Transform for Physically Disabled Persons
Journal of Medical Systems
Volume 36, Issue 4 , pp 2225-2234
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Facial expression recognition
- Discrete wavelet transform
- Orthogonal wavelet family
- Artificial neural network
- k-nearest neighborhood
- Linear discriminant analysis
- Industry Sectors