HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method

Zhong, Yuanchang; Sun, Lili; Ge, Chenhao; Fan, Huilian

doi:10.3390/sym13020228

Cited by 13 publications

(12 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that the combination of the locations of the landmarks and the HOG features of the landmarks are significant assets that can be used to classify emotions because the HOG calculates both the horizontal and vertical components of the gradient magnitude and the direction of each individual pixel. According to [33][34][35], the HOG can effectively extract facial features. Unlike existing approaches where the HOG is calculated using the entire face, only the area around the detected landmarks defined by our proposed infinity shape were used to calculate the HOG.…”

Section: Resultsmentioning

confidence: 99%

Emotion Recognition for Partial Faces Using a Feature Vector Technique

Khoeun

Chophuk

Chinnasarn

2022

Sensors

View full text Add to dashboard Cite

Wearing a facial mask is indispensable in the COVID-19 pandemic; however, it has tremendous effects on the performance of existing facial emotion recognition approaches. In this paper, we propose a feature vector technique comprising three main steps to recognize emotions from facial mask images. First, a synthetic mask is used to cover the facial input image. With only the upper part of the image showing, and including only the eyes, eyebrows, a portion of the bridge of the nose, and the forehead, the boundary and regional representation technique is applied. Second, a feature extraction technique based on our proposed rapid landmark detection method employing the infinity shape is utilized to flexibly extract a set of feature vectors that can effectively indicate the characteristics of the partially occluded masked face. Finally, those features, including the location of the detected landmarks and the Histograms of the Oriented Gradients, are brought into the classification process by adopting CNN and LSTM; the experimental results are then evaluated using images from the CK+ and RAF-DB data sets. As the result, our proposed method outperforms existing cutting-edge approaches and demonstrates better performance, achieving 99.30% and 95.58% accuracy on CK+ and RAF-DB, respectively.

show abstract

Section: Resultsmentioning

confidence: 99%

Emotion Recognition for Partial Faces Using a Feature Vector Technique

Khoeun

Chophuk

Chinnasarn

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Findings Limitations [19] Histogram of oriented gradientensembles with shared representation (HOG-ESR), a novel algorithm, combine HOG and ESRs for face expression identification.…”

Section: Methodsmentioning

confidence: 99%

“…Dance teaching that takes into account students' emotional states improves accuracy through the use of emotion detection. To better engage students and create a good learning environment, teachers might change their strategy by recognizing emotions that include delight in equation (19).…”

Section: A Accuracymentioning

confidence: 99%

Research on dance teaching methods based on emotion recognition and emotion analysis

Jing Wang

2024

jes

View full text Add to dashboard Cite

In sports dance (SPD) education, it is common to give excessive value on technique while ignoring emotion. This issue causes emotions and motion to be poorly integrated, which negatively impacts the educational result. As a result, this study employs a sensor to gather video data from SPD performers and then extracts the crucial characteristic spots to estimate their poses. This study proposes unique capuchin search-driven bi-directional tuned recurrent network (CS-BRN) architecture to efficiently categorize dancers' emotions. Additionally, the arousal value can be used to categorize the arousal valence (AV) emotional paradigm into various parts. The AV paradigm performs superior in high and low fields, which correlate to exciting, nervous and mild feelings, respectively. We employed metrics to evaluate this study's performance like precision (90%,) accuracy (96%), F1-score (92%), recall (91%), The findings of the trial demonstrate that the suggested method has a high degree of emotional recognition accuracy and can precisely identify the crucial moments in the performers' technical motions. This study significantly aided in the emotional identification and alleviation of SPD participants during the education procedure by precisely identifying the crucial parts of their technical motion presentations.

show abstract

“…Therefore, automated recognition systems are usually used to deal with the task. Apart from different algorithms and approaches proposed by scientists from leading research centers [57][58][59], fully commercial systems such as Azure Face API, Face++, Noldus FaceReader, or the iMotion module for Facial Expression Analysis [60][61][62] are available on the market. In our work, we used the latter of the mentioned systems, the iMotion module, which uses the Affectiva algorithm [63] to detect AU movements and the underlying emotions.…”

Section: Analysis Of Emotionsmentioning

confidence: 99%

Eye-Tracker Study of Influence of Affective Disruptive Content on User’s Visual Attention and Emotional State

Lewandowska

Rejer

Bortko

et al. 2022

Sensors

View full text Add to dashboard Cite

When reading interesting content or searching for information on a website, the appearance of a pop-up advertisement in the middle of the screen is perceived as irritating by a recipient. Interrupted cognitive processes are considered unwanted by the user but desired by advertising providers. Diverting visual attention away from the main content is intended to focus the user on the appeared disruptive content. Is the attempt to reach the user by any means justified? In this study, we examined the impact of pop-up emotional content on user reactions. For this purpose, a cognitive experiment was designed where a text-reading task was interrupted by two types of affective pictures: positive and negative ones. To measure the changes in user reactions, an eye-tracker (for analysis of eye movements and changes in gaze points) and an iMotion Platform (for analysis of face muscles’ movements) were used. The results confirm the impact of the type of emotional content on users’ reactions during cognitive process interruptions and indicate that the negative impact of cognitive process interruptions on the user can be reduced. The negative content evoked lower cognitive load, narrower visual attention, and lower irritation compared to positive content. These results offer insight on how to provide more efficient Internet advertising.

show abstract

HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method

Cited by 13 publications

References 48 publications

Emotion Recognition for Partial Faces Using a Feature Vector Technique

Emotion Recognition for Partial Faces Using a Feature Vector Technique

Research on dance teaching methods based on emotion recognition and emotion analysis

Eye-Tracker Study of Influence of Affective Disruptive Content on User’s Visual Attention and Emotional State

Contact Info

Product

Resources

About