Gender and Counterstereotypical Facial Expressions of Emotion in Crowdfunded Microlending

2021 ◽  
pp. 104225872110297
Author(s):  
Blakley C. Davis ◽  
Benjamin J. Warnick ◽  
Aaron H. Anglin ◽  
Thomas H. Allison
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Expression Analysis ◽  
Gender Stereotypes ◽  
Expectancy Violation ◽  
Facial Expression Analysis ◽  
Computer Aided ◽  
Facial Expressions Of Emotion

Crowdfunded microlending research implies that both communal and agentic characteristics are valued. These characteristics, however, are often viewed as being at odds with one another due to their association with gender stereotypes. Drawing upon expectancy violation theory and research on gender stereotypes, we theorize that gender-counterstereotypical facial expressions of emotion provide a means for entrepreneurs to project “missing” agentic or communal characteristics. Leveraging computer-aided facial expression analysis to analyze entrepreneur photographs from 43,210 microloan appeals, we show that women benefit from stereotypically masculine facial expressions of anger and disgust, whereas men benefit from stereotypically feminine facial expressions of sadness and happiness.

Spontaneous Facial Expression Analysis and Synthesis for Interactive Facial Animation

2011 ◽  
pp. 20-37
Author(s):  
Yongmian Zhang ◽  
Jixu Chen ◽  
Yan Tong ◽  
Qiang Ji
Keyword(s):  
Facial Expression ◽  
Bayesian Network ◽  
Facial Expressions ◽  
Expression Analysis ◽  
Facial Animation ◽  
Head Movements ◽  
Perceptual Quality ◽  
Facial Action ◽  
Facial Expression Analysis ◽  
Analysis And Synthesis

This chapter describes a probabilistic framework for faithful reproduction of spontaneous facial expressions on a synthetic face model in a real time interactive application. The framework consists of a coupled Bayesian network (BN) to unify the facial expression analysis and synthesis into one coherent structure. At the analysis end, we cast the facial action coding system (FACS) into a dynamic Bayesian network (DBN) to capture relationships between facial expressions and the facial motions as well as their uncertainties and dynamics. The observations fed into the DBN facial expression model are measurements of facial action units (AUs) generated by an AU model. Also implemented by a DBN, the AU model captures the rigid head movements and nonrigid facial muscular movements of a spontaneous facial expression. At the synthesizer, a static BN reconstructs the Facial Animation Parameters (FAPs) and their intensity through the top-down inference according to the current state of facial expression and pose information output by the analysis end. The two BNs are connected statically through a data stream link. The novelty of using the coupled BN brings about several benefits. First, a facial expression is inferred through both spatial and temporal inference so that the perceptual quality of animation is less affected by the misdetection of facial features. Second, more realistic looking facial expressions can be reproduced by modeling the dynamics of human expressions in facial expression analysis. Third, very low bitrate (9 bytes per frame) in data transmission can be achieved.

scholarly journals Facial Expression Analysis using Convolutional Neural Networks

2020 ◽  
Vol 9 (4) ◽  
pp. 1142-1146
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
Expression Analysis ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Real Time System ◽  
Facial Expression Analysis

Human feelings are mental conditions of sentiments that emerge immediately as opposed to cognitive exertion. Some of the basic feelings are happy, angry, neutral, sad and surprise. These internal feelings of a person are reflected on the face as Facial Expressions. This paper presents a novel methodology for Facial Expression Analysis which will aid to develop a facial expression recognition system. This system can be used in real time to classify five basic emotions. The recognition of facial expressions is important because of its applications in many domains such as artificial intelligence, security and robotics. Many different approaches can be used to overcome the problems of Facial Expression Recognition (FER) but the best suited technique for automated FER is Convolutional Neural Networks(CNN). Thus, a novel CNN architecture is proposed and a combination of multiple datasets such as FER2013, FER+, JAFFE and CK+ is used for training and testing. This helps to improve the accuracy and develop a robust real time system. The proposed methodology confers quite good results and the obtained accuracy may give encouragement and offer support to researchers to build better models for Automated Facial Expression Recognition systems.

Facial Action Recognition in 2D and 3D

2014 ◽  
pp. 167-186 ◽  
Author(s):  
Michel Valstar ◽  
Stefanos Zafeiriou ◽  
Maja Pantic
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Expression Analysis ◽  
Coding System ◽  
Recording Equipment ◽  
Facial Action ◽  
Facial Expression Analysis ◽  
Challenges And Opportunities ◽  
Almost All ◽  
2D And 3D

Automatic Facial Expression Analysis systems have come a long way since the earliest approaches in the early 1970s. We are now at a point where the first systems are commercially applied, most notably smile detectors included in digital cameras. As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has received significant and sustained attention within the field. Over the past 30 years, psychologists and neuroscientists have conducted extensive research on various aspects of human behaviour using facial expression analysis coded in terms of FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Mainly due to the cost effectiveness of existing recording equipment, until recently almost all work conducted in this area involves 2D imagery, despite their inherent problems relating to pose and illumination variations. In order to deal with these problems, 3D recordings are increasingly used in expression analysis research. In this chapter, the authors give an overview of 2D and 3D FACS recognition, and summarise current challenges and opportunities.

A Thermal Blended Facial Expression Analysis and Recognition System Using Deformed Thermal Facial Areas

2021 ◽  
Author(s):  
Priya Saha ◽  
Debotosh Bhattacharjee ◽  
Barin Kumar De ◽  
Mita Nasipuri
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Expression Analysis ◽  
State Of The Art ◽  
Recognition System ◽  
Facial Expression Analysis ◽  
Face Images ◽  
Art Methods ◽  
Facial Area ◽  
Thermal Face

There are many research works in visible as well as thermal facial expression analysis and recognition. Several facial expression databases have been designed in both modalities. However, little attention has been given for analyzing blended facial expressions in the thermal infrared spectrum. In this paper, we have introduced a Visual-Thermal Blended Facial Expression Database (VTBE) that contains visual and thermal face images with both basic and blended facial expressions. The database contains 12 posed blended facial expressions and spontaneous six basic facial expressions in both modalities. In this paper, we have proposed Deformed Thermal Facial Area (DTFA) in thermal expressive face image and make an analysis to differentiate between basic and blended expressions using DTFA. Here, the fusion of DTFA and Deformed Visual Facial Area (DVFA) has been proposed combining the features of both modalities and experiments and has been conducted on this new database. However, to show the effectiveness of our proposed approach, we have compared our method with state-of-the-art methods using USTC-NVIE database. Experiment results reveal that our approach is superior to state-of-the-art methods.

Capacity-Free Automatic Processing of Facial Expressions of Emotion

2020 ◽  
Author(s):  
Joshua W Maxwell ◽  
Eric Ruthruff ◽  
michael joseph
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Face Processing ◽  
Emotional Expressions ◽  
Facial Expression Of Emotion ◽  
Expression Of Emotion ◽  
Facial Expressions Of Emotion ◽  
Correspondence Effect ◽  
Experimental Logic ◽  
Face Task

Are facial expressions of emotion processed automatically? Some authors have not found this to be the case (Tomasik et al., 2009). Here we revisited the question with a novel experimental logic – the backward correspondence effect (BCE). In three dual-task studies, participants first categorized a sound (Task 1) and then indicated the location of a target face (Task 2). In Experiment 1, Task 2 required participants to search for one facial expression of emotion (angry or happy). We observed positive BCEs, indicating that facial expressions of emotion bypassed the central attentional bottleneck and thus were processed in a capacity-free, automatic manner. In Experiment 2, we replicated this effect but found that morphed emotional expressions (which were used by Tomasik) were not processed automatically. In Experiment 3, we observed similar BCEs for another type of face processing previously shown to be capacity-free – identification of familiar faces (Jung et al., 2013). We conclude that facial expressions of emotion are identified automatically when sufficiently unambiguous.

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