scholarly journals A Prediction of Emotions for Recognition of Facial Expressions using Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1076-1079
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
Expression Recognition ◽  
Learning Approaches ◽  
Basic Emotions ◽  
Recognition Of Facial Expressions ◽  
Network Method ◽  
Machine Interface

Automated facial expression recognition can greatly improve the human–machine interface. Many deep learning approaches have been applied in recent years due to their outstanding recognition accuracy after training with large amounts of data. In this research, we enhanced Convolutional Neural Network method to recognize 6 basic emotions and compared some pre processing methods to show the influences of its in CNN performance. The preprocessing methods are :resizing, mean, normalization, standard deviation, scaling and edge detection . Face detection as single pre-processing phase achieved significant result with 100 % of accuracy, compared with another pre-processing phase and raw data.

Recognition of Emotional Facial Expressions: The Role of Facial and Contextual Information in the Accuracy of Recognition

2012 ◽  
Vol 110 (1) ◽  
pp. 338-350 ◽  
Author(s):  
Mariano Chóliz ◽  
Enrique G. Fernández-Abascal
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
Contextual Information ◽  
Central Area ◽  
Expression Recognition ◽  
Emotional Facial Expressions ◽  
Basic Emotions

Recognition of emotional facial expressions is a central area in the psychology of emotion. This study presents two experiments. The first experiment analyzed recognition accuracy for basic emotions including happiness, anger, fear, sadness, surprise, and disgust. 30 pictures (5 for each emotion) were displayed to 96 participants to assess recognition accuracy. The results showed that recognition accuracy varied significantly across emotions. The second experiment analyzed the effects of contextual information on recognition accuracy. Information congruent and not congruent with a facial expression was displayed before presenting pictures of facial expressions. The results of the second experiment showed that congruent information improved facial expression recognition, whereas incongruent information impaired such recognition.

The current challenges of automatic recognition of facial expressions: A systematic review

2020 ◽  
Vol 33 (3-6) ◽  
pp. 113-138
Author(s):  
Audrey Masson ◽  
Guillaume Cazenave ◽  
Julien Trombini ◽  
Martine Batt
Keyword(s):  
Systematic Review ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Future Research ◽  
Expression Recognition ◽  
Review Of The Literature ◽  
Significant Progress ◽  
Recognition Of Facial Expressions ◽  
The Subject ◽  
Current Systems

In recent years, due to its great economic and social potential, the recognition of facial expressions linked to emotions has become one of the most flourishing applications in the field of artificial intelligence, and has been the subject of many developments. However, despite significant progress, this field is still subject to many theoretical debates and technical challenges. It therefore seems important to make a general inventory of the different lines of research and to present a synthesis of recent results in this field. To this end, we have carried out a systematic review of the literature according to the guidelines of the PRISMA method. A search of 13 documentary databases identified a total of 220 references over the period 2014–2019. After a global presentation of the current systems and their performance, we grouped and analyzed the selected articles in the light of the main problems encountered in the field of automated facial expression recognition. The conclusion of this review highlights the strengths, limitations and main directions for future research in this field.

A Study on Improved Real-Time Eye Region Detection Method for More Realistic Facial Expressions Recognition

2011 ◽  
Vol 268-270 ◽  
pp. 471-475
Author(s):  
Sungmo Jung ◽  
Seoksoo Kim
Keyword(s):  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Detection Method ◽  
Expression Recognition ◽  
Improved Method ◽  
Region Detection ◽  
Recognition Of Facial Expressions ◽  
The Face ◽  
Facial Expressions Recognition ◽  
Eye Region

Many 3D films use technologies of facial expression recognition. In order to use the existing technologies, a large number of markers shall be attached to a face, a camera is fixed in front of the face, and movements of the markers are calculated. However, the markers calculate only the changes in regions where the markers are attached, which makes difficult realistic recognition of facial expressions. Therefore, this study extracted a preliminary eye region in 320*240 by defining specific location values of the eye. And the final eye region was selected from the preliminary region. This study suggests an improved method of detecting an eye region, reducing errors arising from noise.

scholarly journals Blurred Facial Expression Recognition System by Using Convolution Neural Network

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 804-816
Author(s):  
Elaf J. Al Taee ◽  
Qasim Mohammed Jasim
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Cognitive Activity ◽  
Recognition System ◽  
Vital Role ◽  
Convolution Neural Network ◽  
Expression Recognition ◽  
Recognition Of Facial Expressions

A facial expression is a visual impression of a person's situations, emotions, cognitive activity, personality, intention and psychopathology, it has an active and vital role in the exchange of information and communication between people. In machines and robots which dedicated to communication with humans, the facial expressions recognition play an important and vital role in communication and reading of what is the person implies, especially in the field of health. For that the research in this field leads to development in communication with the robot. This topic has been discussed extensively, and with the progress of deep learning and use Convolution Neural Network CNN in image processing which widely proved efficiency, led to use CNN in the recognition of facial expressions. Automatic system for Facial Expression Recognition FER require to perform detection and location of faces in a cluttered scene, feature extraction, and classification. In this research, the CNN used for perform the process of FER. The target is to label each image of facial into one of the seven facial emotion categories considered in the JAFFE database. JAFFE facial expression database with seven facial expression labels as sad, happy, fear, surprise, anger, disgust, and natural are used in this research. We trained CNN with different depths using gray-scale images from the JAFFE database.The accuracy of proposed system was 100%.

scholarly journals DRER: Deep Learning–Based Driver’s Real Emotion Recognizer

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2166
Author(s):  
Geesung Oh ◽  
Junghwan Ryu ◽  
Euiseok Jeong ◽  
Ji Hyun Yang ◽  
Sungwook Hwang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Emotional State ◽  
Electrodermal Activity ◽  
Intelligent Vehicles ◽  
Expression Recognition ◽  
Recognition Model ◽  
Increase In Accuracy

In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation.

scholarly journals Efficient recognition of facial expressions does not require motor simulation

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Gilles Vannuscorps ◽  
Michael Andres ◽  
Alfonso Caramazza
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Visual Analysis ◽  
Expression Recognition ◽  
Motor Simulation ◽  
Recognition Of Facial Expressions ◽  
Motor Representations ◽  
Efficient Recognition

What mechanisms underlie facial expression recognition? A popular hypothesis holds that efficient facial expression recognition cannot be achieved by visual analysis alone but additionally requires a mechanism of motor simulation — an unconscious, covert imitation of the observed facial postures and movements. Here, we first discuss why this hypothesis does not necessarily follow from extant empirical evidence. Next, we report experimental evidence against the central premise of this view: we demonstrate that individuals can achieve normotypical efficient facial expression recognition despite a congenital absence of relevant facial motor representations and, therefore, unaided by motor simulation. This underscores the need to reconsider the role of motor simulation in facial expression recognition.

scholarly journals Schizotypy is associated with difficulties detecting emotional facial expressions

2021 ◽  
Vol 8 (11) ◽  
Author(s):  
Shota Uono ◽  
Wataru Sato ◽  
Reiko Sawada ◽  
Sayaka Kawakami ◽  
Sayaka Yoshimura ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Task ◽  
Clinical Population ◽  
Expression Recognition ◽  
Emotional Facial Expressions ◽  
Visual Change ◽  
Recognition Of Facial Expressions ◽  
Age And Sex

People with schizophrenia or subclinical schizotypal traits exhibit impaired recognition of facial expressions. However, it remains unclear whether the detection of emotional facial expressions is impaired in people with schizophrenia or high levels of schizotypy. The present study examined whether the detection of emotional facial expressions would be associated with schizotypy in a non-clinical population after controlling for the effects of IQ, age, and sex. Participants were asked to respond to whether all faces were the same as quickly and as accurately as possible following the presentation of angry or happy faces or their anti-expressions among crowds of neutral faces. Anti-expressions contain a degree of visual change that is equivalent to that of normal emotional facial expressions relative to neutral facial expressions and are recognized as neutral expressions. Normal expressions of anger and happiness were detected more rapidly and accurately than their anti-expressions. Additionally, the degree of overall schizotypy was negatively correlated with the effectiveness of detecting normal expressions versus anti-expressions. An emotion–recognition task revealed that the degree of positive schizotypy was negatively correlated with the accuracy of facial expression recognition. These results suggest that people with high levels of schizotypy experienced difficulties detecting and recognizing emotional facial expressions.

scholarly journals Highly Robust and Wearable Facial Expression Recognition via Deep-Learning-Assisted, Soft Epidermal Electronics

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Meiqi Zhuang ◽  
Lang Yin ◽  
Youhua Wang ◽  
Yunzhao Bai ◽  
Jian Zhan ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Real Life ◽  
Small Sample ◽  
Expression Recognition ◽  
Time Dynamic ◽  
Epidermal Electronics

The facial expressions are a mirror of the elusive emotion hidden in the mind, and thus, capturing expressions is a crucial way of merging the inward world and virtual world. However, typical facial expression recognition (FER) systems are restricted by environments where faces must be clearly seen for computer vision, or rigid devices that are not suitable for the time-dynamic, curvilinear faces. Here, we present a robust, highly wearable FER system that is based on deep-learning-assisted, soft epidermal electronics. The epidermal electronics that can fully conform on faces enable high-fidelity biosignal acquisition without hindering spontaneous facial expressions, releasing the constraint of movement, space, and light. The deep learning method can significantly enhance the recognition accuracy of facial expression types and intensities based on a small sample. The proposed wearable FER system is superior for wide applicability and high accuracy. The FER system is suitable for the individual and shows essential robustness to different light, occlusion, and various face poses. It is totally different from but complementary to the computer vision technology that is merely suitable for simultaneous FER of multiple individuals in a specific place. This wearable FER system is successfully applied to human-avatar emotion interaction and verbal communication disambiguation in a real-life environment, enabling promising human-computer interaction applications.

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