![]() Selsnick, W., Baraniuk, R.G., Kingsburg, N.G.: The Dual – Tree Complex Wavelet Transform – a coherent framework for multiscale signal and image processing. In: International Conference on Intelligent Computation Technology and Automation, pp. Zhou, S., Liang, X.-M., Zhu, C.: Support Vector Clustering of Facial Expression Features. In: 5th International Conference on Future Information Technology (FutureTech). Kazmi, S.B., Ul-Ain, Q., Arfan Jaffar, M.: Wavelets Based Facial Expression Recognition Using a Bank of Neural Networks. In: International Congress on Image and Signal Processing (CISP), pp. Shi, D., Jiang, J.: The Method of Facial Expression Recognition Based on DWT-PCA/LDA. Engineering Applications of Artificial Intelligence 21(7), 1056–1064 (2008) IEEE (2011)īashyal, S., Venayagamoorthy, G.K.: Recognition of Facial Expressions Using Gabor Wavelets and Learning Vector Quantization. In: Seventh International Conference on Signal Image Technology & Internet-Based Systems, pp. Gupta, S.K., Agrwal, S., Meena, Y.K., Nain, N.: A Hybrid Method of Feature Extraction for Facial Expression Recognition. In: International Conference on Computing, Communication and Applications (ICCCA). Thomas, N., Mathew, M.: Facial Expression Recognition System Using Neural Network and MATLAB. In: International Conference on Emerging Trends in Engineering and Technology, vol. 22, pp. Kharat, G.U., Dudul, S.V.: Neural Network Classifier for Human Emotion Recognition from Facial Expressions Using Discrete Cosine Transform. Kharat, G.U., Dudul, S.V.: Human Emotion Recognition System Using Optimally Designed SVM with Different Facial Feature Extraction Techniques. DT-CWT outperforms Gabor wavelet technique for both classifiers. The results suggest that cropped face approach gives better results compared to whole face approach. ![]() ![]() The results are compared with those existing in literature and prove to be more efficient. The overall average accuracy is 93% and 80% for NN and KNN respectively. These methods are combined in different possible combinations with the two aforesaid approaches and the databases to explore their efficiency. ![]() 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. 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. Feature extraction and classification are the two main steps in an emotion recognition system. 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. ![]()
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