scholarly journals Recognition of peripherally exposed emotional expressions

2018 ◽  
Vol 11 (2) ◽  
pp. 16-33 ◽  
Author(s):  
A.V. Zhegallo
Keyword(s):  
Facial Expression ◽  
Latent Period ◽  
Facial Expressions ◽  
High Accuracy ◽  
Face Image ◽  
Emotional Expressions ◽  
Sufficient Condition ◽  
Emotional Facial Expressions ◽  
The Face ◽  
Exposition Time

The study investigates the specifics of recognition of emotional facial expressions in peripherally exposed facial expressions, while exposition time was shorter compared to the duration of the latent period of a saccade towards the exposed image. The study showed that recognition of peripherical perception reproduces the patterns of the choice of the incorrect responses. The mutual mistaken recognition is common for the facial expressions of a fear, anger and surprise. In the case of worsening of the conditions of recognition, calmness and grief as facial expression were included in the complex of a mutually mistakenly identified expressions. The identification of the expression of happiness deserves a special attention, because it can be mistakenly identified as different facial expression, but other expressions are never recognized as happiness. Individual accuracy of recognition varies from 0.29 to 0.80. The sufficient condition of a high accuracy in recognition was the recognition of the facial expressions using peripherical vision without making a saccade in the direction of the face image exposed.

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 Auxiliary Models

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 227 ◽  
Author(s):  
Yingying Wang ◽  
Yibin Li ◽  
Yong Song ◽  
Xuewen Rong
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
State Of The Art ◽  
Face Image ◽  
Great Success ◽  
Expression Recognition ◽  
Input Information ◽  
The Face ◽  
Feature Information

In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one’s emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods.

Effects of Briefly Presented Masked Emotional Facial Expressions on Gaze Behavior: An Eye-Tracking Study

2018 ◽  
Vol 122 (4) ◽  
pp. 1432-1448 ◽  
Author(s):  
Charlott Maria Bodenschatz ◽  
Anette Kersting ◽  
Thomas Suslow
Keyword(s):  
Eye Movements ◽  
Facial Expression ◽  
Eye Tracking ◽  
Facial Expressions ◽  
Affective Priming ◽  
Facial Features ◽  
Emotional Facial Expressions ◽  
Mouth Region ◽  
The Face ◽  
Eye Region

Orientation of gaze toward specific regions of the face such as the eyes or the mouth helps to correctly identify the underlying emotion. The present eye-tracking study investigates whether facial features diagnostic of specific emotional facial expressions are processed preferentially, even when presented outside of subjective awareness. Eye movements of 73 healthy individuals were recorded while completing an affective priming task. Primes (pictures of happy, neutral, sad, angry, and fearful facial expressions) were presented for 50 ms with forward and backward masking. Participants had to evaluate subsequently presented neutral faces. Results of an awareness check indicated that participants were subjectively unaware of the emotional primes. No affective priming effects were observed but briefly presented emotional facial expressions elicited early eye movements toward diagnostic regions of the face. Participants oriented their gaze more rapidly to the eye region of the neutral mask after a fearful facial expression. After a happy facial expression, participants oriented their gaze more rapidly to the mouth region of the neutral mask. Moreover, participants dwelled longest on the eye region after a fearful facial expression, and the dwell time on the mouth region was longest for happy facial expressions. Our findings support the idea that briefly presented fearful and happy facial expressions trigger an automatic mechanism that is sensitive to the distribution of relevant facial features and facilitates the orientation of gaze toward them.

scholarly journals Examining the visual screening patterns of emotional facial expressions with gender, age and lateralization

2013 ◽  
Vol 6 (4) ◽  
Author(s):  
Banu Cangöz ◽  
Arif Altun ◽  
Petek Aşkar ◽  
Zeynel Baran ◽  
Sacide Güzin Mazman
Keyword(s):  
Eye Tracking ◽  
Facial Expressions ◽  
Fixation Duration ◽  
Hemispheric Lateralization ◽  
Emotional Expressions ◽  
Emotional Facial Expressions ◽  
Face Area ◽  
The Face ◽  
Visual Screening ◽  
Emotional Face

The main objective of the study is to investigate the effects of age of model, gender of observer, and lateralization on visual screening patterns while looking at the emotional facial expressions. Data were collected through eye tracking methodology. The areas of interests were set to include eyes, nose and mouth. The selected eye metrics were first fixation duration, fixation duration and fixation count. Those eye tracking metrics were recorded for different emotional expressions (sad, happy, neutral), and conditions (the age of model, part of face and lateralization). The results revealed that participants looked at the older faces shorter in time and fixated their gaze less compared to the younger faces. This study also showed that when participants were asked to passively look at the face expressions, eyes were important areas in determining sadness and happiness, whereas eyes and noise were important in determining neutral expression. The longest fixated face area was on eyes for both young and old models. Lastly, hemispheric lateralization hypothesis regarding emotional face process was supported.

scholarly journals Left Hemisphere Dominance for Negative Facial Expressions: The Influence of Task

2021 ◽  
Vol 15 ◽  
Author(s):  
E. Darcy Burgund
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Left Hemisphere ◽  
Right Hemisphere ◽  
Detection Task ◽  
Emotional Expressions ◽  
Emotional Facial Expressions ◽  
Identification Task ◽  
Hemisphere Dominance ◽  
Expression Processing

Major theories of hemisphere asymmetries in facial expression processing predict right hemisphere dominance for negative facial expressions of disgust, fear, and sadness, however, some studies observe left hemisphere dominance for one or more of these expressions. Research suggests that tasks requiring the identification of six basic emotional facial expressions (angry, disgusted, fearful, happy, sad, and surprised) are more likely to produce left hemisphere involvement than tasks that do not require expression identification. The present research investigated this possibility in two experiments that presented six basic emotional facial expressions to the right or left hemisphere using a divided-visual field paradigm. In Experiment 1, participants identified emotional expressions by pushing a key corresponding to one of six labels. In Experiment 2, participants detected emotional expressions by pushing a key corresponding to whether an expression was emotional or not. In line with predictions, fearful facial expressions exhibited a left hemisphere advantage during the identification task but not during the detection task. In contrast to predictions, sad expressions exhibited a left hemisphere advantage during both identification and detection tasks. In addition, happy facial expressions exhibited a left hemisphere advantage during the detection task but not during the identification task. Only angry facial expressions exhibited a right hemisphere advantage, and this was only observed when data from both experiments were combined. Together, results highlight the influence of task demands on hemisphere asymmetries in facial expression processing and suggest a greater role for the left hemisphere in negative expressions than predicted by previous theories.

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.

Perception of Photographic-Quality Caricatures of Emotional Facial Expressions

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 28-28
Author(s):  
A J Calder ◽  
A W Young ◽  
D Rowland ◽  
D R Gibbenson ◽  
B M Hayes ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Mental Representation ◽  
Reaction Times ◽  
Target Face ◽  
Emotional Expressions ◽  
Emotional Facial Expressions ◽  
Basic Emotions ◽  
Happy Expression ◽  
Neutral Expression

G Rhodes, S E Brennan, S Carey (1987 Cognitive Psychology19 473 – 497) and P J Benson and D I Perrett (1991 European Journal of Cognitive Psychology3 105 – 135) have shown that computer-enhanced (caricatured) representations of familiar faces are named faster and rated as better likenesses than veridical (undistorted) representations. Here we have applied Benson and Perrett's graphic technique to examine subjects' perception of enhanced representations of photographic-quality facial expressions of basic emotions. To enhance a facial expression the target face is compared to a norm or prototype face, and, by exaggerating the differences between the two, a caricatured image is produced; reducing the differences results in an anticaricatured image. In experiment 1 we examined the effect of degree of caricature and types of norm on subjects' ratings for ‘intensity of expression’. Three facial expressions (fear, anger, and sadness) were caricatured at seven levels (−50%, −30%, −15%, 0%, +15%, +30%, and +50%) relative to three different norms; (1) an average norm prepared by blending pictures of six different emotional expressions; (2) a neutral expression norm; and (3) a different expression norm (eg anger caricatured relative to a happy expression). Irrespective of norm, the caricatured expressions were rated as significantly more intense than the veridical images. Furthermore, for the average and neutral norm sets, the anticaricatures were rated as significantly less intense. We also examined subjects' reaction times to recognise caricatured (−50%, 0%, and +50%) representations of six emotional facial expressions. The results showed that the caricatured images were identified fastest, followed by the veridical, and then anticaricatured images. Hence the perception of facial expression and identity is facilitated by caricaturing; this has important implications for the mental representation of facial expressions.

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.

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.

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