Emotion Recognition from Facial Expressions Using Frequency Domain Techniques
An emotion recognition system from facial expression is used for recognizing expressions from the facial images and classifying them into one of the six basic emotions. Feature extraction and classification are the two main steps in an emotion recognition system. In this paper, two approaches viz., cropped face and whole face methods for feature extraction are implemented separately on the images taken from Cohn-Kanade (CK) and JAFFE database. Transform techniques such as Dual – Tree Complex Wavelet Transform (DT-CWT) and Gabor Wavelet Transform are considered for the formation of feature vectors along with Neural Network (NN) and K-Nearest Neighbor (KNN) as the Classifiers. These methods are combined in different possible combinations with the two aforesaid approaches and the databases to explore their efficiency. The overall average accuracy is 93% and 80% for NN and KNN respectively. The results are compared with those existing in literature and prove to be more efficient. The results suggest that cropped face approach gives better results compared to whole face approach. DT-CWT outperforms Gabor wavelet technique for both classifiers.
KeywordsFrequency Domain Feature Extraction Classification DT-CWT Gabor Wavelet Neural network KNN
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- 1.Kharat, G.U., Dudul, S.V.: Human Emotion Recognition System Using Optimally Designed SVM with Different Facial Feature Extraction Techniques. WSEAS Transactions on Computers 7, 650–659 (2008)Google Scholar
- 2.Kharat, G.U., Dudul, S.V.: Neural Network Classifier for Human Emotion Recognition from Facial Expressions Using Discrete Cosine Transform. In: International Conference on Emerging Trends in Engineering and Technology, vol. 22, pp. 653–658. IEEE (2008)Google Scholar
- 3.Thomas, N., Mathew, M.: Facial Expression Recognition System Using Neural Network and MATLAB. In: International Conference on Computing, Communication and Applications (ICCCA). IEEE (2012)Google Scholar
- 4.Gupta, S.K., Agrwal, S., Meena, Y.K., Nain, N.: A Hybrid Method of Feature Extraction for Facial Expression Recognition. In: Seventh International Conference on Signal Image Technology & Internet-Based Systems, pp. 422–425. IEEE (2011)Google Scholar
- 6.Shi, D., Jiang, J.: The Method of Facial Expression Recognition Based on DWT-PCA/LDA. In: International Congress on Image and Signal Processing (CISP), pp. 1970–1974. IEEE (2010)Google Scholar
- 7.Kazmi, S.B., Ul-Ain, Q., Arfan Jaffar, M.: Wavelets Based Facial Expression Recognition Using a Bank of Neural Networks. In: 5th International Conference on Future Information Technology (FutureTech). IEEE (June 2010)Google Scholar
- 8.Zhou, S., Liang, X.-M., Zhu, C.: Support Vector Clustering of Facial Expression Features. In: International Conference on Intelligent Computation Technology and Automation, pp. 811–815. IEEE (2008)Google Scholar
- 10.Li, Y., Ruan, Q., Li, X.: Facial Expression Recognition Based on Complex Wavelet Transform. In: IET 3rd International Conference on Wireless, Mobile and Multimedia Networks. IEEE (January 2010)Google Scholar