{"title":"基于二维平稳小波变换和灰度共生的面部表情识别MatrixP@13-17","authors":"Nikunja Bihari Kar, Korra Sathya Babu","doi":"10.1145/3232651.3232664","DOIUrl":null,"url":null,"abstract":"This paper presents an automated facial expression recognition (FER) system based on two dimensional stationary wavelet transform (2D-SWT) and gray-level co-occurrence matrix (GLCM). The proposed scheme employs 2D-SWT to decompose the image into a set of sub-bands. Then GLCM features are obtained from the 2D-SWT sub-bands. Subsequently, linear discriminant analysis (LDA) is harnessed to select the most relevant features. Finally, these features are used for classification of facial emotions using least squares variant of support vector machine (LS-SVM) with radial basis function (RBF) kernel. The performance of the pro-posed system is evaluated on two standard datasets namely, Extended Cohn-Kanade (CK+) and Japanese female facial expression (JAFFE). Experimental results based on 5-fold cross validation strategy indicate that the proposed scheme earns an accuracy of 96.72% and 99.79% over CK+ and JAFFE dataset respectively, which are superior to other competent schemes.","PeriodicalId":365064,"journal":{"name":"Proceedings of the 1st International Conference on Control and Computer Vision","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Facial Expression Recognition using 2D Stationary Wavelet Transform and Gray-Level Co-occurrence MatrixP@13-17\",\"authors\":\"Nikunja Bihari Kar, Korra Sathya Babu\",\"doi\":\"10.1145/3232651.3232664\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an automated facial expression recognition (FER) system based on two dimensional stationary wavelet transform (2D-SWT) and gray-level co-occurrence matrix (GLCM). The proposed scheme employs 2D-SWT to decompose the image into a set of sub-bands. Then GLCM features are obtained from the 2D-SWT sub-bands. Subsequently, linear discriminant analysis (LDA) is harnessed to select the most relevant features. Finally, these features are used for classification of facial emotions using least squares variant of support vector machine (LS-SVM) with radial basis function (RBF) kernel. The performance of the pro-posed system is evaluated on two standard datasets namely, Extended Cohn-Kanade (CK+) and Japanese female facial expression (JAFFE). Experimental results based on 5-fold cross validation strategy indicate that the proposed scheme earns an accuracy of 96.72% and 99.79% over CK+ and JAFFE dataset respectively, which are superior to other competent schemes.\",\"PeriodicalId\":365064,\"journal\":{\"name\":\"Proceedings of the 1st International Conference on Control and Computer Vision\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1st International Conference on Control and Computer Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3232651.3232664\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st International Conference on Control and Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3232651.3232664","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Facial Expression Recognition using 2D Stationary Wavelet Transform and Gray-Level Co-occurrence MatrixP@13-17
This paper presents an automated facial expression recognition (FER) system based on two dimensional stationary wavelet transform (2D-SWT) and gray-level co-occurrence matrix (GLCM). The proposed scheme employs 2D-SWT to decompose the image into a set of sub-bands. Then GLCM features are obtained from the 2D-SWT sub-bands. Subsequently, linear discriminant analysis (LDA) is harnessed to select the most relevant features. Finally, these features are used for classification of facial emotions using least squares variant of support vector machine (LS-SVM) with radial basis function (RBF) kernel. The performance of the pro-posed system is evaluated on two standard datasets namely, Extended Cohn-Kanade (CK+) and Japanese female facial expression (JAFFE). Experimental results based on 5-fold cross validation strategy indicate that the proposed scheme earns an accuracy of 96.72% and 99.79% over CK+ and JAFFE dataset respectively, which are superior to other competent schemes.