Tsinghua facial expression database – A database of facial expressions in Chinese young and older women and men: Development and validation

Facial Expression Recognition has become the preliminary research area due to its importance in human-computer interaction. Facial Expressions conveys the major part of information so it has vast applications in various fields. Many techniques have been developed in the literature but there is still a need to make the current expression recognition methods efficient. This paper represents proposed framework for face detection and recognizing six universal facial expressions such as happy, anger, disgust, fear, surprise, sad along with neutral face. Viola-Jones method and Face Landmark Detection method are used for face detection. Histogram of oriented gradients is used for feature extraction due to its superiority over other methods. To reduce the dimensionality of features Principal Component Analysis is used so that the maximum variation is preserved. Canberra distance classifier is used for classifying the expressions into different emotions. The proposed method is applied on Japanese Female Facial Expression Database and have evaluated that the proposed method outperforms many state-of-the-art techniques.

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Development and validation of a facial expression database based on the dimensional and categorical model of emotions

Cognition & Emotion ◽

10.1080/02699931.2017.1419936 ◽

2018 ◽

Vol 32 (8) ◽

pp. 1663-1670 ◽

Cited By ~ 6

Author(s):

Tomomi Fujimura ◽

Hiroyuki Umemura

Keyword(s):

Facial Expression ◽

Categorical Model ◽

Development And Validation ◽

Facial Expression Database

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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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