The Experts below are selected from a list of 519099 Experts worldwide ranked by ideXlab platform
Matti Pietikäinen - One of the best experts on this subject based on the ideXlab platform.
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riesz based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
British Machine Vision Conference, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
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BMVC - Riesz-based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
Procedings of the British Machine Vision Conference 2015, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
Xiaohua Huang - One of the best experts on this subject based on the ideXlab platform.
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riesz based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
British Machine Vision Conference, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
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BMVC - Riesz-based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
Procedings of the British Machine Vision Conference 2015, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
Abhinav Dhall - One of the best experts on this subject based on the ideXlab platform.
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riesz based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
British Machine Vision Conference, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
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BMVC - Riesz-based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
Procedings of the British Machine Vision Conference 2015, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
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ICPR - Group Expression intensity estimation in videos via Gaussian Processes
2012Co-Authors: Abhinav Dhall, Roland GoeckeAbstract:Facial Expression analysis has been a very active field of research in recent years. This paper proposes a method for finding the apex of an Expression, e.g. happiness, in a video containing a Group of people based on Expression intensity estimation. The proposed method is directly applied to video summarisation based on Group happiness and timestamps; further, a novel Gaussian Process Regression based Expression intensity estimation method is described. To demonstrate its performance, experiments on smile intensity estimation are performed and compared to other regression based techniques. The smile intensity estimator is extended to Group happiness intensity estimation. The proposed intensity estimator can be extended easily for other Expressions. The experiments are performed on an ‘in the wild’ dataset. Quantitative results are presented for comparison of our happiness-intensity detector. A user study was also conducted to verify the results of the proposed method.
Guoying Zhao - One of the best experts on this subject based on the ideXlab platform.
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riesz based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
British Machine Vision Conference, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
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BMVC - Riesz-based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
Procedings of the British Machine Vision Conference 2015, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
Roland Goecke - One of the best experts on this subject based on the ideXlab platform.
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riesz based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
British Machine Vision Conference, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
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BMVC - Riesz-based volume local binary pattern and a novel Group Expression model for Group happiness intensity analysis
Procedings of the British Machine Vision Conference 2015, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a Group of people. For Group emotional intensity analysis, feature extraction and Group Expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a Group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new Group Expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial Expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new Group Expression model with the new feature. Our experimental results demonstrate the promising performance for Group happiness intensity analysis.
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ICPR - Group Expression intensity estimation in videos via Gaussian Processes
2012Co-Authors: Abhinav Dhall, Roland GoeckeAbstract:Facial Expression analysis has been a very active field of research in recent years. This paper proposes a method for finding the apex of an Expression, e.g. happiness, in a video containing a Group of people based on Expression intensity estimation. The proposed method is directly applied to video summarisation based on Group happiness and timestamps; further, a novel Gaussian Process Regression based Expression intensity estimation method is described. To demonstrate its performance, experiments on smile intensity estimation are performed and compared to other regression based techniques. The smile intensity estimator is extended to Group happiness intensity estimation. The proposed intensity estimator can be extended easily for other Expressions. The experiments are performed on an ‘in the wild’ dataset. Quantitative results are presented for comparison of our happiness-intensity detector. A user study was also conducted to verify the results of the proposed method.
