On Facial Expression Recognition Benchmarks

Owusu, Ebenezer; Kumi, Jacqueline Asor; Appati, Justice Kwame

doi:10.1155/2021/9917246

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…Feature recognition is considered the identification of the image. This paper implements the four types of feature recognition [ 25 ], namely, Geometric feature-based approach Holistic feature-based approach Hybrid Approach Template-based technique. …”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm

Arora

Chaubey²,

Raman

et al. 2022

Computational Intelligence and Neuroscience

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Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.

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Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm

Arora

Chaubey²,

Raman

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Accurate analysis of facial expressions can effectively assess the learning emotions of college students. Research on human expressions in various literatures [11][12][13] mainly focuses on six basic expressions: disgust, sadness, fear, anger, surprise, and happiness, with expression databases primarily sourced from movie videos or dynamic collections. However, expressions in educational environments differ from those in movies or daily life scenarios, especially during online learning, where students generally do not exhibit very exaggerated expressions and emotional changes, such as sadness, anger, or fear, making the common classification of expressions unsuitable for the online learning field.…”

Section: Building An Online Learning Expression Database For College ...mentioning

confidence: 99%

Enhancing Emotion Recognition in College Students' Online Learning: A Research on Integrating Feature Fusion and Attention Mechanisms

Sun,

Liu,

Qian

2024

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Aiming at key challenges in emotional recognition for college students in online learning, including the lack of experimental data sets, the imperfect emotion classification system, and the poor robustness of emotion recognition algorithms, this paper constructs an emotion recognition model for college students' online learning based on feature fusion and attention mechanism. First, based on face recognition technology, the Convolutional Neural Network (CNN) deep features, Histogram of Oriented Gradients (HOG) texture features, and Scale-Invariant Feature Transform (SIFT) features of images are extracted and fused, because three feature extraction algorithms have different feature extraction capabilities and have certain robustness to changes in image lighting, rotation, scale, and other factors, fusion can make feature vectors more comprehensive and rich in information, thereby improving the accuracy and robustness of detection and recognition; second, a ResNet network is constructed to complete the basic classification of learning expressions and verify the experimental results; In order to enhance the ability of deep learning methods to learn discriminative features from noisy signals and further improve classification accuracy, then, by combining the channel attention mechanism and soft thresholding to improve the ResNet network, a Deep Residual Shrinkage Network (DRSN) is constructed to achieve emotion recognition of college students' online learning; finally, through a horizontal comparison experiment of multiple different network structures, the effectiveness of the soft threshold attention mechanism is verified. This method obtains more complete facial expression features for emotion classification through feature fusion, and combines the channel attention mechanism module. The recognition accuracy of DRSN network is 84.12%, which is about 4.17% higher than the original ResNet network (79.95%). The application of this research can assess the concentration of college students in online learning and measure the degree of emotional engagement of college students. Through the analysis of model result data, teachers can understand the course content that college students are interested in, adjust teaching plans according to the learning status of students, help realize personalized teaching, and consolidate teaching results.

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“…A system for describing facial motions using a set of standardised units called Action Units (AUs). While automatic FER has received the greatest attention from studies [7] noting that "each individual communicates his emotion by his manner," this is no simple undertaking. In this work, together with the neutral state, we provide a unique fusionbased technique for identifying human facial expressions of the six main emotions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Network Feature Fusion Facial Emotion Recognition using Nonparametric Method with Augmentation

Devi,

Sharma

2023

IJRITCC

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Facial expression emotion identification and prediction is one of the most difficult problems in computer science. Pre-processing and feature extraction are crucial components of the more conventional methods. For the purpose of emotion identification and prediction using 2D facial expressions, this study targets the Face Expression Recognition dataset and shows the real implementation or assessment of learning algorithms such as various CNNs. Due to its vast potential in areas like artificial intelligence, emotion detection from facial expressions has become an essential requirement. Many efforts have been done on the subject since it is both a challenging and fascinating challenge in computer vision. The focus of this study is on using a convolutional neural network supplemented with data to build a facial emotion recognition system. This method may use face images to identify seven fundamental emotions, including anger, contempt, fear, happiness, neutrality, sadness, and surprise. As well as improving upon the validation accuracy of current models, a convolutional neural network that takes use of data augmentation, feature fusion, and the NCA feature selection approach may assist solve some of their drawbacks. Researchers in this area are focused on improving computer predictions by creating methods to read and codify facial expressions. With deep learning's striking success, many architectures within the framework are being used to further the method's efficacy. We highlight the contributions dealt with, the architecture and databases used, and demonstrate the development by contrasting the offered approaches and the outcomes produced. The purpose of this study is to aid and direct future researchers in the subject by reviewing relevant recent studies and offering suggestions on how to further the field. An innovative feature-based transfer learning technique is created using the pre-trained networks MobileNetV2 and DenseNet-201. The suggested system's recognition rate is 75.31%, which is a significant improvement over the results of the prior feature fusion study.

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On Facial Expression Recognition Benchmarks

Cited by 9 publications

References 45 publications

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm

Enhancing Emotion Recognition in College Students' Online Learning: A Research on Integrating Feature Fusion and Attention Mechanisms

Multi-Network Feature Fusion Facial Emotion Recognition using Nonparametric Method with Augmentation

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