Can Unhappy Pictures Enhance the Effect of Personas? A User Experiment

There has been little research into whether a persona's picture should portray a happy or unhappy individual. We report a user experiment with 235 participants, testing the effects of happy and unhappy image styles on user perceptions, engagement, and personality traits attributed to personas using a mixed-methods analysis. Results indicate that the participant's perceptions of the persona's realism and pain point severity increase with the use of unhappy pictures. In contrast, personas with happy pictures are perceived as more extroverted, agreeable, open, conscientious, and emotionally stable. The participants’ proposed design ideas for the personas scored more lexical empathy scores for happy personas. There were also significant perception changes along with the gender and ethnic lines regarding both empathy and perceptions of pain points. Implications are the facial expression in the persona profile can affect the perceptions of those employing the personas. Therefore, persona designers should align facial expressions with the task for which the personas will be employed. Generally, unhappy images emphasize realism and pain point severity, and happy images invoke positive perceptions.

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The effect of personality and social context on willingness to share emotion information on social media

10.31234/osf.io/g8ndz ◽

2018 ◽

Author(s):

Damien Dupré ◽

Nicole Andelic ◽

Anna Zajac ◽

Gawain Morrison ◽

Gary John McKeown

Keyword(s):

Social Media ◽

Social Context ◽

Facial Expression ◽

Personality Traits ◽

Emotion Recognition ◽

Facial Expressions ◽

Personal Information ◽

Facial Emotion ◽

Emotional Information ◽

New Sensors

Sharing personal information is an important way of communicating on social media. Among the information possibly shared, new sensors and tools allow people to share emotion information via facial emotion recognition. This paper questions whether people are prepared to share personal information such as their own emotion on social media. In the current study we examined how factors such as felt emotion, motivation for sharing on social media as well as personality affected participants’ willingness to share self-reported emotion or facial expression online. By carrying out a GLMM analysis, this study found that participants’ willingness to share self-reported emotion and facial expressions was influenced by their personality traits and the motivation for sharing their emotion information that they were given. From our results we can conclude that the estimated level of privacy for certain emotional information, such as facial expression, is influenced by the motivation for sharing the information online.

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Are You “Gazing” at Me? How Others' Gaze Direction and Facial Expression Influence Gaze Perception and Postural Control

Frontiers in Psychology ◽

10.3389/fpsyg.2021.730953 ◽

2021 ◽

Vol 12 ◽

Author(s):

Angélique Lebert ◽

Laurence Chaby ◽

Amandine Guillin ◽

Samuel Chekroun ◽

Dorine Vergilino-Perez

Keyword(s):

Facial Expression ◽

Postural Control ◽

Personality Traits ◽

Facial Expressions ◽

Postural Stability ◽

Center Of Pressure ◽

Gaze Direction ◽

Gaze Perception ◽

Direct Gaze ◽

Approach Avoidance

In everyday life, interactions between humans are generally modulated by the value attributed to the situation, which partly relies on the partner's behavior. A pleasant or cooperating partner may trigger an approach behavior in the observer, while an unpleasant or threatening partner may trigger an avoidance behavior. In this context, the correct interpretation of other's intentions is crucial to achieve satisfying social interactions. Social cues such as gaze direction and facial expression are both fundamental and interrelated. Typically, whenever gaze direction and facial expression of others communicate the same intention, it enhances both the interlocutor's gaze direction and the perception of facial expressions (i.e., shared signal hypothesis). For instance, an angry face with a direct gaze is perceived as more intense since it represents a threat to the observer. In this study, we propose to examine how the combination of others' gaze direction (direct or deviated) and emotional facial expressions (i.e., happiness, fear, anger, sadness, disgust, and neutrality) influence the observer's gaze perception and postural control. Gaze perception was indexed by the cone of direct gaze (CoDG) referring to the width over which an observer feels someone's gaze is directed at them. A wider CoDG indicates that the observer perceived the face as looking at them over a wider range of gaze directions. Conversely, a narrower CoDG indicates a decrease in the range of gaze directions perceived as direct. Postural control was examined through the center of pressure displacements reflecting postural stability and approach-avoidance tendencies. We also investigated how both gaze perception and postural control may vary according to participants' personality traits and emotional states (e.g., openness, anxiety, etc.). Our results confirmed that gaze perception is influenced by emotional faces: a wider CoDGs was observed with angry and disgusted faces while a narrower CoDG was observed for fearful faces. Furthermore, facial expressions combined with gaze direction influence participants' postural stability but not approach-avoidance behaviors. Results are discussed in the light of the approach-avoidance model, by considering how some personality traits modulate the relation between emotion and posture.

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Beyond personality traits: Which facial expressions imply dominance in two-person interaction scenes?

Emotion ◽

10.1037/emo0000286 ◽

2018 ◽

Vol 18 (6) ◽

pp. 872-885 ◽

Cited By ~ 2

Author(s):

Yoshiyuki Ueda ◽

Sakiko Yoshikawa

Keyword(s):

Personality Traits ◽

Facial Expressions

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The face value of feedback: Facial behavior is shaped by goals and punishments during interaction with dynamic faces

10.31234/osf.io/9vwhq ◽

2020 ◽

Author(s):

Jonathan Yi ◽

Philip Pärnamets ◽

Andreas Olsson

Keyword(s):

Decision Making ◽

Facial Expression ◽

Facial Expressions ◽

Large Body ◽

Mechanistic Explanation ◽

Experimental Manipulation ◽

Corrugator Supercilii ◽

Dynamic Faces ◽

Facial Behavior ◽

Reinforcement Learning Models

Responding appropriately to others’ facial expressions is key to successful social functioning. Despite the large body of work on face perception and spontaneous responses to static faces, little is known about responses to faces in dynamic, naturalistic situations, and no study has investigated how goal directed responses to faces are influenced by learning during dyadic interactions. To experimentally model such situations, we developed a novel method based on online integration of electromyography (EMG) signals from the participants’ face (corrugator supercilii and zygomaticus major) during facial expression exchange with dynamic faces displaying happy and angry facial expressions. Fifty-eight participants learned by trial-and-error to avoid receiving aversive stimulation by either reciprocate (congruently) or respond opposite (incongruently) to the expression of the target face. Our results validated our method, showing that participants learned to optimize their facial behavior, and replicated earlier findings of faster and more accurate responses in congruent vs. incongruent conditions. Moreover, participants performed better on trials when confronted with smiling, as compared to frowning, faces, suggesting it might be easier to adapt facial responses to positively associated expressions. Finally, we applied drift diffusion and reinforcement learning models to provide a mechanistic explanation for our findings which helped clarifying the underlying decision-making processes of our experimental manipulation. Our results introduce a new method to study learning and decision-making in facial expression exchange, in which there is a need to gradually adapt facial expression selection to both social and non-social reinforcements.

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Capacity-Free Automatic Processing of Facial Expressions of Emotion

10.31234/osf.io/jpr6t ◽

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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The face of future: Face expressions during future thinking

Quarterly Journal of Experimental Psychology ◽

10.1177/1747021821992991 ◽

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.

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

Applied Sciences ◽

10.3390/app11041428 ◽

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.

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Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽

10.3390/s21062003 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2003 ◽

Cited By ~ 1

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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Effects of Pedagogical Agents on Students’ Mathematics Performance: A Comparison Between Two Versions

Journal of Educational Computing Research ◽

10.1177/0735633117722494 ◽

2017 ◽

Vol 56 (5) ◽

pp. 701-722 ◽

Cited By ~ 3

Author(s):

Rex P. Bringula ◽

Ian Clement O. Fosgate ◽

Neil Peter R. Garcia ◽

Josf Luinico M. Yorobe

Keyword(s):

Experimental Study ◽

Facial Expression ◽

Prior Knowledge ◽

Facial Expressions ◽

Mathematics Learning ◽

Mathematics Performance ◽

Pedagogical Agents ◽

Pedagogical Agent ◽

Mathematics Scores ◽

Mathematics Problems

This experimental study investigated the effects of the use of two versions of a pedagogical agent named personal instructing agent (PIA) on the mathematics performance of students. The first version exhibits synthetic facial expressions while the second version does not exhibit facial expression (i.e., neutral facial expression). Two groups of students with the same levels of prior knowledge in mathematics utilized two different versions of PIA. The first group—the facial group—utilized a PIA that provides textual and facial expressions feedback (happy, sad, surprise, and neutral facial expressions). The second group—the nonfacial group—used the same software except that PIA only exhibited neutral facial expression. The study showed that the mathematics scores of the students in the facial group significantly improved as compared with those who are in the nonfacial group. The posttest scores of the facial group were found significantly higher than those of the nonfacial group. The study showed that PIA that exhibited synthetic facial expressions improved students’ mathematics learning. It is concluded that synthetic facial expressions and textual feedback of pedagogical agent can be utilized to help students learn to solve mathematics problems. Limitations and recommendations are also presented.

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Komparasi Metode Klasifikasi untuk Deteksi Ekspresi Wajah Dengan Fitur Facial Landmark

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021834434 ◽

2021 ◽

Vol 8 (5) ◽

pp. 949

Author(s):

Fitra A. Bachtiar ◽

Muhammad Wafi

Keyword(s):

Facial Expression ◽

Facial Expressions ◽

Cross Validation ◽

Classification Model ◽

Expression Recognition ◽

Classification Methods ◽

Human Machine Interaction ◽

Facial Landmark ◽

Total Data ◽

Machine Interaction

Human machine interaction, khususnya pada facial behavior mulai banyak diperhatikan untuk dapat digunakan sebagai salah satu cara untuk personalisasi pengguna. Kombinasi ekstraksi fitur dengan metode klasifikasi dapat digunakan agar sebuah mesin dapat mengenali ekspresi wajah. Akan tetapi belum diketahui basis metode klasifikasi apa yang tepat untuk digunakan. Penelitian ini membandingkan tiga metode klasifikasi untuk melakukan klasifikasi ekspresi wajah. Dataset ekspresi wajah yang digunakan pada penelitian ini adalah JAFFE dataset dengan total 213 citra wajah yang menunjukkan 7 (tujuh) ekspresi wajah. Ekspresi wajah pada dataset tersebut yaitu anger, disgust, fear, happy, neutral, sadness, dan surprised. Facial Landmark digunakan sebagai ekstraksi fitur wajah. Model klasifikasi yang digunakan pada penelitian ini adalah ELM, SVM, dan k-NN. Masing masing model klasifikasi akan dicari nilai parameter terbaik dengan menggunakan 80% dari total data. 5- fold cross-validation digunakan untuk mencari parameter terbaik. Pengujian model dilakukan dengan 20% data dengan metode evaluasi akurasi, F1 Score, dan waktu komputasi. Nilai parameter terbaik pada ELM adalah menggunakan 40 hidden neuron, SVM dengan nilai = 105 dan 200 iterasi, sedangkan untuk k-NN menggunakan 3 k tetangga. Hasil uji menggunakan parameter tersebut menunjukkan ELM merupakan algoritme terbaik diantara ketiga model klasifikasi tersebut. Akurasi dan F1 Score untuk klasifikasi ekspresi wajah untuk ELM mendapatkan nilai akurasi sebesar 0.76 dan F1 Score 0.76, sedangkan untuk waktu komputasi membutuhkan waktu 6.97´10-3 detik. AbstractHuman-machine interaction, especially facial behavior is considered to be use in user personalization. Feature extraction and classification model combinations can be used for a machine to understand the human facial expression. However, which classification base method should be used is not yet known. This study compares three classification methods for facial expression recognition. JAFFE dataset is used in this study with a total of 213 facial images which shows seven facial expressions. The seven facial expressions are anger, disgust, fear, happy, neutral, sadness, dan surprised. Facial Landmark is used as a facial component features. The classification model used in this study is ELM, SVM, and k-NN. The hyperparameter of each model is searched using 80% of the total data. 5-fold cross-validation is used to find the hyperparameter. The testing is done using 20% of the data and evaluated using accuracy, F1 Score, and computation time. The hyperparameter for ELM is 40 hidden neurons, SVM with = 105 and 200 iteration, while k-NN used 3 k neighbors. The experiment results show that ELM outperforms other classification methods. The accuracy and F1 Score achieved by ELM is 0.76 and 0.76, respectively. Meanwhile, time computation takes 6.97 10-3 seconds.

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