Automatic Interpretation of Affective Facial Expressions in the Context of Interpersonal Interaction

Barakova, Emilia; Gorbunov, Roman D.; Rauterberg, Matthias

doi:10.1109/thms.2015.2419259

Cited by 31 publications

(13 citation statements)

References 20 publications

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“…The proposed method outperforms the state-of-the-art by up to 8% in terms of emotion prediction accuracy. Genetic programming can also be used for FER [46], specifically for searching and optimizing the parameters defined for determining the location, intensity, and type of the emotional events, and how these are linked to each emotion. Tested on the Mars-500 database, the proposed method predicts the six basic emotions with over 75% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Gavrilescu

Vizireanu

2017

J Image Video Proc.

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We propose a novel three-layered neural network-based architecture for predicting the Sixteen Personality Factors from facial features analyzed using Facial Action Coding System. The proposed architecture is built on three layers: a base layer where the facial features are extracted from each video frame using a multi-state face model and the intensity levels of 27 Action Units (AUs) are computed, an intermediary level where an AU activity map is built containing all AUs' intensity levels fetched from the base layer in a frame-by-frame manner, and a top layer consisting of 16 feed-forward neural networks trained via backpropagation which analyze the patterns in the AU activity map and compute scores from 1 to 10, predicting each of the 16 personality traits. We show that the proposed architecture predicts with an accuracy of over 80%: warmth, emotional stability, liveliness, social boldness, sensitivity, vigilance, and tension. We also show there is a significant relationship between the emotions elicited to the analyzed subjects and high prediction accuracy obtained for each of the 16 personality traits as well as notable correlations between distinct sets of AUs present at high-intensity levels and increased personality trait prediction accuracy. The system converges to a stable result in no more than 1 min, making it faster and more practical than the Sixteen Personality Factors Questionnaire and suitable for real-time monitoring of people's personality traits.

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Section: Related Workmentioning

confidence: 99%

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Gavrilescu

Vizireanu

2017

J Image Video Proc.

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“…The data were collected during two game interactions: an on-line (web based) experiment as reported in [15] and the dataset that we collected during MARS-500 isolation experiment. We collected three kinds of data: behavior in a cooperative computer game, self-assessment questionnaires, and video rec-ords of facial expressions during game play [3]. In the web based experiment, 27 participants took part.…”

Section: Analysis Of the Decisions Of The Playersmentioning

confidence: 99%

A cognitive model of social preferences in group interactions

Gorbunov

Rauterberg²,

Barakova³

2019

ICA

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Modeling the interactions in groups is becoming increasingly important in many application domains such as the design of interactive systems and social robots. Since human interactants do not always make rational choices, a good model of their social motivations is needed to explain the strategies of the interactants that are often influenced by social factors and preferences, the feeling of fairness and understanding the need for cooperation. We propose a cognitive model of social preferences of three or more interactants that are engaged in a collaborative game. The game strategies of the interactants are modeled with cooperation ratios and utility functions. We developed a new generalized utility -based approach to model the cooperation and fairness in multiplayer interactions, which uses three utility parameters. In two-person games, as it has been investigated by others, it is impossible to distinguish between fairness and cooperation in the decisions of a given player. We show that in n-person games (n>2), and with the use of the proposed utility-based approach, it is possible to distinguish between fairness and cooperation. This makes the proposed approach suitable for more detailed analysis of group interactions in a game setting, which can better explain the social motivation of the interactants, than existing utility models and models that utilize on cooperation ratios. We show that the proposed generalization makes the newly proposed utility function less sensitive to the payoffs of one player if the size of the group grows, and test it with data from the MARS-500 isolation experiment.

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“…Artificial neural networks are particularly effective in analyzing nonlinear processes in real-time; so, many researchers use them to identify human facial expressions. In [25,26], the use of genetic algorithms is considered; in [27] the use of cascaded continuous regression; in [28] the use of shallow neural networks. Reference [6] describes a fuzzy system for emotional intent classifying, while [29] describes the affect estimation by audio stream using ensemble of ordinal classifiers.…”

Section: Related Workmentioning

confidence: 99%

The Extended Multidimensional Neo-Fuzzy System and Its Fast Learning in Pattern Recognition Tasks

Bodyanskiy

Kulishova

Chala

2018

Data

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Methods of machine learning and data mining are becoming the cornerstone in information technologies with real-time image and video recognition methods getting more and more attention.While computational system architectures are getting larger and more complex, their learning methods call for changes, as training datasets often reach tens and hundreds of thousands of samples, therefore increasing the learning time of such systems. It is possible to reduce computational costs by tuning the system structure to allow fast, high accuracy learning algorithms to be applied. This paper proposes a system based on extended multidimensional neo-fuzzy units and its learning algorithm designed for data streams processing tasks. The proposed learning algorithm, based on the information entropy criterion, has significantly improved the system approximating capabilities. Experiments have confirmed the efficiency of the proposed system in solving real-time video stream recognition tasks.

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Automatic Interpretation of Affective Facial Expressions in the Context of Interpersonal Interaction

Cited by 31 publications

References 20 publications

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

A cognitive model of social preferences in group interactions

The Extended Multidimensional Neo-Fuzzy System and Its Fast Learning in Pattern Recognition Tasks

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