Abstract: An emotion analysis and recognition system including an automated recognition of bodily expression of emotion (ARBEE) system is described. The system may include program instructions executable by a processor to: receive a plurality of body movement models, each body movement model generated based on a crowdsourced body language dataset, calculate at least one evaluation metric for each body movement model, select a highest ranked body movement model based on the at least one metric calculated for each body movement model, combine the highest ranked body movement model with at least one other body movement model of the plurality of body movement models, calculate at least one evaluation metric for each combination of body movement models, and determine a highest ranked combination of body movement models to predict a bodily expression of emotion.

Abstract: An emotion analysis and recognition system including an automated recognition of bodily expression of emotion (ARBEE) system is described. The system may include program instructions executable by a processor to: receive a plurality of body movement models, each body movement model generated based on a crowdsourced body language dataset, calculate at least one evaluation metric for each body movement model, select a highest ranked body movement model based on the at least one metric calculated for each body movement model, combine the highest ranked body movement model with at least one other body movement model of the plurality of body movement models, calculate at least one evaluation metric for each combination of body movement models, and determine a highest ranked combination of body movement models to predict a bodily expression of emotion.

Abstract: An emotion analysis and recognition system including an automated recognition of bodily expression of emotion (ARBEE) system is described. The system may include program instructions executable by a processor to: receive a plurality of body movement models, each body movement model generated based on a crowdsourced body language dataset, calculate at least one evaluation metric for each body movement model, select a highest ranked body movement model based on the at least one metric calculated for each body movement model, combine the highest ranked body movement model with at least one other body movement model of the plurality of body movement models, calculate at least one evaluation metric for each combination of body movement models, and determine a highest ranked combination of body movement models to predict a bodily expression of emotion.

Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.

Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.

Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.

Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.

Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.

Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.

Abstract: Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.