Robust Facial Expression Recognition Based on Local Directional Pattern
Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance‐based feature descriptor, the local...
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| Published in: | ETRI journal Vol. 32; no. 5; pp. 784 - 794 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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한국전자통신연구원
01.10.2010
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| ISSN: | 1225-6463, 2233-7326 |
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| Abstract | Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance‐based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well‐known machine learning methods, template matching and support vector machine, are used for classification using the Cohn‐Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance‐based feature descriptors. |
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| AbstractList | Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors. KCI Citation Count: 139 Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance‐based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well‐known machine learning methods, template matching and support vector machine, are used for classification using the Cohn‐Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance‐based feature descriptors. |
| Author | Jabid, Taskeed Chae, Oksam Kabir, Md. Hasanul |
| Author_xml | – sequence: 1 givenname: Taskeed surname: Jabid fullname: Jabid, Taskeed – sequence: 2 givenname: Md. Hasanul surname: Kabir fullname: Kabir, Md. Hasanul – sequence: 3 givenname: Oksam surname: Chae fullname: Chae, Oksam |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001486695$$DAccess content in National Research Foundation of Korea (NRF) |
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| SubjectTerms | facial expression recognition features extraction Image representation local directional pattern principal component analysis support vector machine 전자/정보통신공학 |
| Title | Robust Facial Expression Recognition Based on Local Directional Pattern |
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