scholarly journals A sad thumbs up: Incongruent gestures and disrupted sensorimotor activity both slow processing of facial expressions

2018 ◽  
Author(s):  
Adrienne Wood ◽  
Jared Martin ◽  
Martha W. Alibali ◽  
Paula Niedenthal
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Face Processing ◽  
Congruency Effect ◽  
The Body ◽  
Hand Gestures ◽  
Affective Meaning ◽  
Sensorimotor Activity ◽  
The Face ◽  
Expression Processing

Recognition of affect expressed in the face is disrupted when the body expresses an incongruent affect. Existing research has documented such interference for universally recognizable bodily expressions. However, it remains unknown whether learned, conventional gestures can interfere with facial expression processing. Study 1 participants (N = 62) viewed videos of facial expressions accompanied by hand gestures and reported the valence of either the face or hand. Responses were slower and less accurate when the face-hand pairing was incongruent compared to congruent. We hypothesized that hand gestures might exert an even stronger influence on facial expression processing when other routes to understanding the meaning of a facial expression, such as with sensorimotor simulation, are disrupted. Participants in Study 2 (N = 127) completed the same task, but the facial mobility of some participants was restricted, which disrupted face processing in prior work. The hand-face congruency effect from Study 1 was replicated. The facial mobility manipulation affected males only, and it did not moderate the congruency effect. The present work suggests the affective meaning of conventional gestures is processed automatically and can interfere with face perception, but perceivers do not seem to rely more on gestures when sensorimotor face processing is disrupted.

scholarly journals Can an android’s posture and movement discriminate against the ambiguous emotion perceived from its facial expressions?

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254905
Author(s):  
Satoshi Yagi ◽  
Yoshihiro Nakata ◽  
Yutaka Nakamura ◽  
Hiroshi Ishiguro
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Muscle Activation ◽  
Mapping Method ◽  
Human Robot Interaction ◽  
The Body ◽  
Whole Body ◽  
Emotional Expressions ◽  
Muscle Activation Patterns ◽  
The Face

Expressing emotions through various modalities is a crucial function not only for humans but also for robots. The mapping method from facial expressions to the basic emotions is widely used in research on robot emotional expressions. This method claims that there are specific facial muscle activation patterns for each emotional expression and people can perceive these emotions by reading these patterns. However, recent research on human behavior reveals that some emotional expressions, such as the emotion “intense”, are difficult to judge as positive or negative by just looking at the facial expression alone. Nevertheless, it has not been investigated whether robots can also express ambiguous facial expressions with no clear valence and whether the addition of body expressions can make the facial valence clearer to humans. This paper shows that an ambiguous facial expression of an android can be perceived more clearly by viewers when body postures and movements are added. We conducted three experiments and online surveys among North American residents with 94, 114 and 114 participants, respectively. In Experiment 1, by calculating the entropy, we found that the facial expression “intense” was difficult to judge as positive or negative when they were only shown the facial expression. In Experiments 2 and 3, by analyzing ANOVA, we confirmed that participants were better at judging the facial valence when they were shown the whole body of the android, even though the facial expression was the same as in Experiment 1. These results suggest that facial and body expressions by robots should be designed jointly to achieve better communication with humans. In order to achieve smoother cooperative human-robot interaction, such as education by robots, emotion expressions conveyed through a combination of both the face and the body of the robot is necessary to convey the robot’s intentions or desires to humans.

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.

The face of future: Face expressions during future thinking

2021 ◽  
pp. 174702182199299
Author(s):  
Mohamad El Haj ◽  
Emin Altintas ◽  
Ahmed A Moustafa ◽  
Abdel Halim Boudoukha
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Emotional Experience ◽  
Future Scenarios ◽  
Future Thinking ◽  
Analysis Software ◽  
Emotional Facial Expressions ◽  
Facial Analysis ◽  
The Face ◽  
Face Expressions

Future thinking, which is the ability to project oneself forward in time to pre-experience an event, is intimately associated with emotions. We investigated whether emotional future thinking can activate emotional facial expressions. We invited 43 participants to imagine future scenarios, cued by the words “happy,” “sad,” and “city.” Future thinking was video recorded and analysed with a facial analysis software to classify whether facial expressions (i.e., happy, sad, angry, surprised, scared, disgusted, and neutral facial expression) of participants were neutral or emotional. Analysis demonstrated higher levels of happy facial expressions during future thinking cued by the word “happy” than “sad” or “city.” In contrast, higher levels of sad facial expressions were observed during future thinking cued by the word “sad” than “happy” or “city.” Higher levels of neutral facial expressions were observed during future thinking cued by the word “city” than “happy” or “sad.” In the three conditions, the neutral facial expressions were high compared with happy and sad facial expressions. Together, emotional future thinking, at least for future scenarios cued by “happy” and “sad,” seems to trigger the corresponding facial expression. Our study provides an original physiological window into the subjective emotional experience during future thinking.

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

scholarly journals Transmitting and Decoding Facial Expressions

2005 ◽  
Vol 16 (3) ◽  
pp. 184-189 ◽  
Author(s):  
Marie L. Smith ◽  
Garrison W. Cottrell ◽  
FrédéAric Gosselin ◽  
Philippe G. Schyns
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Brain Structures ◽  
Human Face ◽  
The Face ◽  
The Brain

This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.

Pain Assessment through Facial Expression

2016 ◽  
pp. 59-80
Author(s):  
Sanjay Kumar Singh ◽  
V. Rastogi ◽  
S. K. Singh
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Pain Assessment ◽  
Medical Psychology ◽  
Important Symptom ◽  
The Face ◽  
Proper Diagnosis ◽  
Heath Care ◽  
Visual Changes

Pain, assumed to be the fifth vital sign, is an important symptom that needs to be adequately assessed in heath care. The visual changes reflected on the face of a person in pain may be apparent for only a few seconds and occur instinctively. Tracking these changes is a difficult and time-consuming process in a clinical setting. This is why it is motivating researchers and experts from medical, psychology and computer fields to conduct inter-disciplinary research in capturing facial expressions. This chapter contains a comprehensive review of technologies in the study of facial expression along with its application in pain assessment. The facial expressions of pain in children's (0-2 years) and in non-communicative patients need to be recognized as they are of utmost importance for proper diagnosis. Well designed computerized methodologies would streamline the process of patient assessment, increasing its accessibility to physicians and improving quality of care.

Neural Responses to Facial Expression and Face Identity in the Monkey Amygdala

2007 ◽  
Vol 97 (2) ◽  
pp. 1671-1683 ◽  
Author(s):  
K. M. Gothard ◽  
F. P. Battaglia ◽  
C. A. Erickson ◽  
K. M. Spitler ◽  
D. G. Amaral
Keyword(s):  
Facial Expression ◽  
Firing Rate ◽  
Face Processing ◽  
Neural Responses ◽  
Firing Rates ◽  
Face Identity ◽  
Relative Contribution ◽  
The Face ◽  
Face Stimuli ◽  
Human Faces

The amygdala is purported to play an important role in face processing, yet the specificity of its activation to face stimuli and the relative contribution of identity and expression to its activation are unknown. In the current study, neural activity in the amygdala was recorded as monkeys passively viewed images of monkey faces, human faces, and objects on a computer monitor. Comparable proportions of neurons responded selectively to images from each category. Neural responses to monkey faces were further examined to determine whether face identity or facial expression drove the face-selective responses. The majority of these neurons (64%) responded both to identity and facial expression, suggesting that these parameters are processed jointly in the amygdala. Large fractions of neurons, however, showed pure identity-selective or expression-selective responses. Neurons were selective for a particular facial expression by either increasing or decreasing their firing rate compared with the firing rates elicited by the other expressions. Responses to appeasing faces were often marked by significant decreases of firing rates, whereas responses to threatening faces were strongly associated with increased firing rate. Thus global activation in the amygdala might be larger to threatening faces than to neutral or appeasing faces.

The Auditory Kuleshov Effect: Multisensory Integration in Movie Editing

Perception ◽  
2016 ◽  
Vol 46 (5) ◽  
pp. 624-631 ◽  
Author(s):  
Andreas M. Baranowski ◽  
H. Hecht
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Multisensory Integration ◽  
Interaction Effect ◽  
Neutral Face ◽  
The Face ◽  
Sad Music ◽  
Ambiguous Situations

Almost a hundred years ago, the Russian filmmaker Lev Kuleshov conducted his now famous editing experiment in which different objects were added to a given film scene featuring a neutral face. It is said that the audience interpreted the unchanged facial expression as a function of the added object (e.g., an added soup made the face express hunger). This interaction effect has been dubbed “Kuleshov effect.” In the current study, we explored the role of sound in the evaluation of facial expressions in films. Thirty participants watched different clips of faces that were intercut with neutral scenes, featuring either happy music, sad music, or no music at all. This was crossed with the facial expressions of happy, sad, or neutral. We found that the music significantly influenced participants’ emotional judgments of facial expression. Thus, the intersensory effects of music are more specific than previously thought. They alter the evaluation of film scenes and can give meaning to ambiguous situations.

scholarly journals Ability of Children with Learning Disabilities and Children with Autism Spectrum Disorder to Recognize Feelings from Facial Expressions and Body Language

2018 ◽  
Vol 8 (2) ◽  
pp. 10 ◽  
Author(s):  
Alev Girli ◽  
Sıla Doğmaz
Keyword(s):  
Autism Spectrum Disorder ◽  
Learning Disabilities ◽  
Facial Expressions ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Body Language ◽  
The Body ◽  
Spectrum Disorder ◽  
Black And White ◽  
The Face

In this study, children with learning disability (LD) were compared with children with autism spectrum disorder(ASD) in terms of identifying emotions from photographs with certain face and body expressions. The sampleconsisted of a total of 82 children aged 7-19 years living in Izmir in Turkey. A total of 6 separate sets of slides,consisting of black and white photographs, were used to assess participants’ ability to identify feelings – 3 sets forfacial expressions, and 3 sets for body language. There were 20 photographs on the face slides and 38 photographson the body language slides. The results of the nonparametric Mann Whitney-U test showed no significant differencebetween the total scores that children received from each of the face and body language slide sets. It was observedthat the children with LD usually looked at the whole photo, while the children with ASD focused especially aroundthe mouth to describe feelings. The results that were obtained were discussed in the context of the literature, andsuggestions were presented.

Sign in / Sign up

Export Citation Format

Share Document