The Experts below are selected from a list of 1550964 Experts worldwide ranked by ideXlab platform
Maja Pantic - One of the best experts on this subject based on the ideXlab platform.
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Automated Facial Expression Analysis
2007Co-Authors: Maja Pantic, Léon J. M. RothkrantzAbstract:Human Emotion Recognition Clips Utilised Expert System was designed to recognise and interpret facial Expressions of the observed person in an automatic way [1]. Still, input to HERCULES has been manually supplied. Through integrating HERCULES into the Integrated System for Facial Expression Recognition (ISFER) a complete process of automatic Analysis of facial Expressions has been achieved [2]. ISFER forms a part of the project Automated System for Non-verbal Communication [3] which is an ongoing project at the Knowledge Based Systems department of Delft University of Technology. The theoretical formulation of the implemented facial Expression recognition has been acquired from FACS [4]. The interpretation of the recognised facial Expression is currently based [1][7] on the recognition of so-called six basic emotions defined by Ekman [5][6]: happiness, sadness, fear, surprise, disgust and anger. Validation of the implemented knowledge and performing of the facial Expression Analysis in a completely automated way, are the main topics of this paper.
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Web-based database for facial Expression Analysis
IEEE International Conference on Multimedia and Expo, ICME 2005, 2005Co-Authors: Maja Pantic, Ron Rademaker, Michel Valstar, Leendert MaatAbstract:In the last decade, the research topic of automatic Analysis of facial Expressions has become a central topic in machine vision research. Nonetheless, there is a glaring lack of a comprehensive, readily accessible reference set of face images that could be used as a basis for benchmarks for efforts in the field. This lack of easily accessible, suitable, common testing resource forms the major impediment to comparing and extending the issues concerned with automatic facial Expression Analysis. In this paper, we discuss a number of issues that make the problem of creating a benchmark facial Expression database difficult. We then present the MMI facial Expression database, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various Expressions of emotion, single and multiple facial muscle activation. It has been built as a Web-based direct-manipulation application, allowing easy access and easy search of the available images. This database represents the most comprehensive reference set of images for studies on facial Expression Analysis to date.
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ICME - Web-based database for facial Expression Analysis
2005 IEEE International Conference on Multimedia and Expo, 1Co-Authors: Maja Pantic, Ron Rademaker, Michel Valstar, Ludo MaatAbstract:In the last decade, the research topic of automatic Analysis of facial Expressions has become a central topic in machine vision research. Nonetheless, there is a glaring lack of a comprehensive, readily accessible reference set of face images that could be used as a basis for benchmarks for efforts in the field. This lack of easily accessible, suitable, common testing resource forms the major impediment to comparing and extending the issues concerned with automatic facial Expression Analysis. In this paper, we discuss a number of issues that make the problem of creating a benchmark facial Expression database difficult. We then present the MMI facial Expression database, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various Expressions of emotion, single and multiple facial muscle activation. It has been built as a Web-based direct-manipulation application, allowing easy access and easy search of the available images. This database represents the most comprehensive reference set of images for studies on facial Expression Analysis to date.
Leendert Maat - One of the best experts on this subject based on the ideXlab platform.
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Web-based database for facial Expression Analysis
IEEE International Conference on Multimedia and Expo, ICME 2005, 2005Co-Authors: Maja Pantic, Ron Rademaker, Michel Valstar, Leendert MaatAbstract:In the last decade, the research topic of automatic Analysis of facial Expressions has become a central topic in machine vision research. Nonetheless, there is a glaring lack of a comprehensive, readily accessible reference set of face images that could be used as a basis for benchmarks for efforts in the field. This lack of easily accessible, suitable, common testing resource forms the major impediment to comparing and extending the issues concerned with automatic facial Expression Analysis. In this paper, we discuss a number of issues that make the problem of creating a benchmark facial Expression database difficult. We then present the MMI facial Expression database, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various Expressions of emotion, single and multiple facial muscle activation. It has been built as a Web-based direct-manipulation application, allowing easy access and easy search of the available images. This database represents the most comprehensive reference set of images for studies on facial Expression Analysis to date.
Mauro Delorenzi - One of the best experts on this subject based on the ideXlab platform.
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a comparison of methods for differential Expression Analysis of rna seq data
BMC Bioinformatics, 2013Co-Authors: Charlotte Soneson, Mauro DelorenziAbstract:Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential Expression Analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential Expression Analysis of RNA-seq data. We conducted an extensive comparison of eleven methods for differential Expression Analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential Expression Analysis perform well under many different conditions, as does the nonparametric SAMseq method.
Ludo Maat - One of the best experts on this subject based on the ideXlab platform.
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ICME - Web-based database for facial Expression Analysis
2005 IEEE International Conference on Multimedia and Expo, 1Co-Authors: Maja Pantic, Ron Rademaker, Michel Valstar, Ludo MaatAbstract:In the last decade, the research topic of automatic Analysis of facial Expressions has become a central topic in machine vision research. Nonetheless, there is a glaring lack of a comprehensive, readily accessible reference set of face images that could be used as a basis for benchmarks for efforts in the field. This lack of easily accessible, suitable, common testing resource forms the major impediment to comparing and extending the issues concerned with automatic facial Expression Analysis. In this paper, we discuss a number of issues that make the problem of creating a benchmark facial Expression database difficult. We then present the MMI facial Expression database, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various Expressions of emotion, single and multiple facial muscle activation. It has been built as a Web-based direct-manipulation application, allowing easy access and easy search of the available images. This database represents the most comprehensive reference set of images for studies on facial Expression Analysis to date.
Ron Rademaker - One of the best experts on this subject based on the ideXlab platform.
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Web-based database for facial Expression Analysis
IEEE International Conference on Multimedia and Expo, ICME 2005, 2005Co-Authors: Maja Pantic, Ron Rademaker, Michel Valstar, Leendert MaatAbstract:In the last decade, the research topic of automatic Analysis of facial Expressions has become a central topic in machine vision research. Nonetheless, there is a glaring lack of a comprehensive, readily accessible reference set of face images that could be used as a basis for benchmarks for efforts in the field. This lack of easily accessible, suitable, common testing resource forms the major impediment to comparing and extending the issues concerned with automatic facial Expression Analysis. In this paper, we discuss a number of issues that make the problem of creating a benchmark facial Expression database difficult. We then present the MMI facial Expression database, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various Expressions of emotion, single and multiple facial muscle activation. It has been built as a Web-based direct-manipulation application, allowing easy access and easy search of the available images. This database represents the most comprehensive reference set of images for studies on facial Expression Analysis to date.
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ICME - Web-based database for facial Expression Analysis
2005 IEEE International Conference on Multimedia and Expo, 1Co-Authors: Maja Pantic, Ron Rademaker, Michel Valstar, Ludo MaatAbstract:In the last decade, the research topic of automatic Analysis of facial Expressions has become a central topic in machine vision research. Nonetheless, there is a glaring lack of a comprehensive, readily accessible reference set of face images that could be used as a basis for benchmarks for efforts in the field. This lack of easily accessible, suitable, common testing resource forms the major impediment to comparing and extending the issues concerned with automatic facial Expression Analysis. In this paper, we discuss a number of issues that make the problem of creating a benchmark facial Expression database difficult. We then present the MMI facial Expression database, which includes more than 1500 samples of both static images and image sequences of faces in frontal and in profile view displaying various Expressions of emotion, single and multiple facial muscle activation. It has been built as a Web-based direct-manipulation application, allowing easy access and easy search of the available images. This database represents the most comprehensive reference set of images for studies on facial Expression Analysis to date.
