Inverting natural facial expressions puzzles you
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This chapter contains sections titled: Design Issues for Facial Animation, Levels of Face Control, Generation of Facial Expressions, Analysis of Facial Expressions, Evaluation of Expressive Behavior, Limitations and Extensions, Summary
Expression (computer science)
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We propose a new animation method for making facial expressions using modified face method.Up to now, there have been many methods of generating animation by a computer.Most of them are, however, too complicated to apply to animate facial expression.We can make many animated sequences by using our method in which variables of modified face method are separated into facial feature variables and expression variables.This paper shows some results of facial expressions, for example, a sequence of “angry”.
Feature (linguistics)
Expression (computer science)
Facial motion capture
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Significant improvements have been recently achieved in both quality and realism of computer generated characters, which are nowadays often very difficult to be distinguished from real ones. However, generating highly realistic facial expressions is still a challenging issue, since synthetic expressions usually follow a repetitive pattern, while in natural faces the same expression is usually produced in similar but not equal ways. In this paper, we propose a method to distinguish between computer generated and natural faces based on facial expressions analysis. In particular, small variations of the facial shape models corresponding to the same expression are used as evidence of synthetic characters.
Variation (astronomy)
Expression (computer science)
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Available methods for synthesizing facial expressions synthesize basic expressions. Few methods synthesize subtle facial expressions such as a knavish smile or a cold smile. We developed a system for synthesizing subtle facial expressions. It uses an interactive interface based on a genetic algorithm. We use the five main parts of the face : eyebrows, eyes, mouth, and contours. These parts are deformed independently. Using this system, subtle facial expressions can be synthesized by iterating a deformation process for each part. Experiments showed that this system can efficiently synthesize subtle facial expressions.
Interface (matter)
Human interface device
Expression (computer science)
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Creating facial expressions manually needs to be enhanced through a set of easy operating rules, which involves adjectives and the manipulating combinations of facial design elements. This article tries to analyse viewers' cognition of artificial facial expressions in an objective and scientific way. We chose four adjectives – ‘satisfied’, ‘sarcastic’, ‘disdainful’, and ‘nervous’ – as the experimental subjects. The manipulative key factors of facial expressions (eyebrows, eyes, pupils, mouth and head rotation) were used to create permutations and combinations and to make 81 stimuli of different facial expressions with a 3-D face model in order to conduct a survey. Next, we used Quantification Theory Type I to find the best combinations that participants agreed on as representing these adjectives. The conclusions of this research are that: (1) there are differences in adopting facial features between creating artificial characters' expressions and recognising real humans' expressions; (2) using survey and statistics can scientifically analyse viewers' cognition of facial expressions with form changing; and (3) the results of this research can promote designers' efficiency in working with subtler facial expressions.
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This paper proposes a method to generate diverse robot facial expressions and facial gestures in order to help long-term HRI. First, nine basic dynamics for diverse robot facial expressions are determined based on the dynamics of human facial expressions and principles of animation for even identical emotions. In the second stage, facial actions are added to express facial gestures such as sniffling or wailing loudly corresponding to sadness, laughing aloud or smiling corresponding to happiness, etc. To evaluate the effectiveness of our approach, we compared the facial expressions of the developed robot when the proposed method is used or not. The results of the survey showed that the proposed method can help robots generate more realistic facial expressions.
Sadness
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The paper aims at drawing an expressive facial caricature by adding facial expressions to an expressionless facial caricature that is drawn using linguistic expressions. The expressive facial caricature drawing procedure has two parts; the expressionless facial caricature drawing part and the facial expressions adding part. The former part has already been completed. In the paper the latter part is described. Facial expressions are added to the expressionless facial caricature using the relationship between the parameter value and facial expression that is obtained by the analysis of the questionnaire data.
Expression (computer science)
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Summary Facial expressions provide crucial information about an individual's internal mood state, but are difficult to quantify. We report the development of a mathematical method for measuring facial expressions, using photographs and a specially designed computer programme.
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Facial expressions are an important way for humans to perceive emotions. The advent of facial action coding systems has enabled the quantification of facial expressions. Moreover, a large amount of annotated data facilitates the performance of deep learning for the spotting and recognition of expressions or micro-expressions. However, the study of video-based expressions or micro-expressions requires coders to have expertise while also familiar with action unit (AU) coding. This paper systematically sorts out the relationship between facial muscles and AU to make more people understand AU coding from the principle. For this purpose, we have made a brief guide to get started as quickly as possible for the beginner to code.
Spotting
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The need test data on facial expression recognition for Indonesian people are not easily obtained and not available in existing databases. Race and ethnic differences can affect the results of facial expression recognition. Testing on facial expressions using untrained data will give more objective results. The purpose of this study is to utilize the Acquiface interface feature in the game Expressions Hunter to get data in the form of images of facial expressions from Indonesian players. Finite State Machine is used to provide behavior to the enemy according to a user action to collect facial expressions by hunting and killing enemies. Acquiface interface is a prototype of one of the features in the game Expression Hunter which functions to record user expressions in accordance with the collection of expressions obtained by users after killing regular enemy monsters or enemy Bosses. RADIATE facial stimulus sets are used as stimuli to generate facial expressions according to the type of expression. The result of recording is a facial expression video file which is then extracted into a sequence of facial expressions. The final image of facial expression is selected from the most expressive sequence of facial expressions (having peak expression).
Expression (computer science)
Facial expression recognition
Interface (matter)
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