A Temporal Approach to Facial Emotion Expression Recognition

Asaju, Christine; Vadapalli, Hima

doi:10.1007/978-3-030-95070-5_18

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Using a single algorithm, the multimodal facial expression is also added to the dataset that has been gathered. Asaju et al [21] used a temporal method to recognize facial emotional expressions. This study introduces a CNNbased deep learning procedure that can implement numerous kinds of emotional acknowledgment in the human body.…”

Section: Literature Surveymentioning

confidence: 99%

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An Efficient Deep Learning with Optimization Algorithm for Emotion Recognition in Social Networks

Suresh

2023

IJACSA

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Emotion recognition, or computers' ability to interpret people's emotional states, is a rapidly expanding topic with many life-improving applications. However, most imagebased emotion recognition algorithms have flaws since people can disguise their emotions by changing their facial expressions. As a result, brain signals are being used to detect human emotions with increased precision. However, most proposed systems could do better because electroencephalogram (EEG) signals are challenging to classify using typical machine learning and deep learning methods. Human-computer interaction, recommendation systems, online learning, and data mining all benefit from emotion recognition in photos. However, there are challenges with removing irrelevant text aspects during emotion extraction. As a consequence, emotion prediction is inaccurate. This paper proposes Radial Basis Function Networks (RBFN) with Blue Monkey Optimization to address such challenges in human emotion recognition (BMO). The proposed RBFN-BMO detects faces on large-scale images before analyzing face landmarks to predict facial expressions for emotional acknowledgment. Patch cropping and neural networks comprise the two stages of the RBFN-BMO. Pre-processing, feature extraction, rating, and organizing are the four categories of the proposed model. In the ranking stage, appropriate features are extracted from the pre-processed information, the data are then classed, and accurate output is obtained from the classification phase. This study compares the results of the proposed RBFN-BMO algorithm to the previous state-of-the-art algorithms using publicly available datasets derived from the RBFN-BMO model. Furthermore, we demonstrated the efficacy of our framework in comparison to previous works. The results show that the projected method can progress the rate of emotion recognition on datasets of various sizes.

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Section: Literature Surveymentioning

confidence: 99%

“…To identify emotional expressions on faces, Asaju et al [24] employed the temporal method. This study integrates a CNN with a deep learning technique to create dissimilar kinds of emotional identification in the human body.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Deep Learning with Optimization Algorithm for Emotion Recognition in Social Networks

Suresh

2023

IJACSA

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“…Asaju et al [ 17 ] in this paper implemented the temporal approach to facial emotional expression recognition. This paper implements the various types of emotional recognition in the human body through a deep learning algorithm using a convolutional neural network.…”

Section: Review Of the Literaturementioning

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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“…To deduce human emotion from non-verbal communication, it is sufficient to record body language . When happy, angry, or surprised, the body typically turns toward the source of those emotions; when scared, the body contracts; when joyful, the arms open wide and speed up; when afraid or sad, the body turns away; (Asaju & Vadapalli, 2022) to name just a few examples. Recent research has demonstrated that we are adept at reading the emotional nuances in others' nonverbal cues and drawing conclusions about their mental states.…”

Section: Introductionmentioning

confidence: 99%

Movements Recognition in the Human Body Based on Deep Learning Strategies

Mahdi

2023

JLMA

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These days, the study of human body movements for the purpose of emotion identification is an absolutely necessary component of social communication. Several different contexts call for the implementation of non-verbal communication strategies such as gestures, eye movements, facial expressions, and body language. Among them, emotion detection based on body movements. It can also identify the emotions of a person even if they are too far away from the camera. Other studies have shown that body language can express emotional states more effectively than words can. In this research study, an emotional state is determined by the human motion of the entire body. The architecture of a deep convolution neural network is used, and multiple parameter settings are considered. Both the University of York's emotion dataset, which includes 15 different kinds of emotions, and dataset of GEMEP corpus, which includes five emotions, can be used to assess the proposed system. The results of the experiments demonstrated that the proposed system has a higher degree of recognition accuracy.

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A Temporal Approach to Facial Emotion Expression Recognition

Cited by 6 publications

References 28 publications

An Efficient Deep Learning with Optimization Algorithm for Emotion Recognition in Social Networks

An Efficient Deep Learning with Optimization Algorithm for Emotion Recognition in Social Networks

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm

Movements Recognition in the Human Body Based on Deep Learning Strategies

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