Efficient Facial Expression Recognition System Based on Geometric Features Using Neural Network

Tripathi, Ankita; Pandey, Shambhavi; Jangir, Hitesh

doi:10.1007/978-981-10-3920-1_18

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…MLP shows better results compared to the decision tree and SVM. It uses backpropagation to train the network [6]. There are several techniques used for preprocessing the images but ROI is found to be more useful.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognizing Facial Expressions Across Cultures Using Gradient Features

Akram¹,

Tariq²,

Ali³

et al. 2023

JICET

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The goal of this research is to provide a useful technique for better facial emotion recognition, especially across cultural boundaries. Although people communicate both verbally and nonverbally, face expressions are crucial in determining verbal communication. The previous human-computer interface did not take into account thus much nonverbal communication. We need a system that can recognise and comprehend the intentions and feelings expressed by social and cultural cues. In this article, we present a technique for categorising facial photos into six different categories of expressions. Three phases make up the approach; in the first, we used viola Jones to edit off all but the face from the original image and create new ones. Then a HOG histogram was used to extract gradient characteristics. Last but not least, we used SVM to classify picture characteristics and got encouraging results. Comparing the outcomes of the suggested method to other cutting-edge approaches, they are astounding. With regard to combined cross-cultural datasets, it offers accuracy of 99.97%.

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Section: Literature Reviewmentioning

confidence: 99%

Recognizing Facial Expressions Across Cultures Using Gradient Features

Akram¹,

Tariq²,

Ali³

et al. 2023

JICET

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show abstract

“…Mimic scholars think that emotion recognition is more objective when it is done automatically. [33][34][35][36][37][38][39][40][41] These authors usually identify numerous methods that can automatically recognize emotions by a person's facial expression.…”

Section: Literature Overviewmentioning

confidence: 99%

Procedure for Identifying Negative Emotional States in Military Personnel

Kudaibergenova

Madaliyeva

Baimoldina

et al. 2023

Bangladesh J Med Sci

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Objective: The development of a paradigm for enhancing the emotional state of service members and a method for identifying their negative states as a result of psychological stresses is of the utmost importance in the context of today’s pressing issues. To that end, the purpose of this study is to develop and subjectively test the effectiveness of a procedure for identifying negative emotional states in service members (cadets from Kazakhstan’s military institutes). Materials and methods: A two-stage procedure is proposed to identify a risk group for negative emotional states. The methodology for identifying negative emotional states is divided into two stages: screening and deep. Results and discussion: It will be discussed how the algorithm performed when tested on a sample of cadets from two military universities in Kazakhstan. According to the results, nearly all the people in the sample studied (approximately 95%) exhibited relatively positive indicators of psychological health, including low levels of depression, aggression, and anxiety. Negative emotional states were present in 5% of the service members. Additional ongoing dynamic assessment and monitoring of the risk group is required. Conclusion: The obtained results provide sufficient evidence to discuss how the proposed algorithm could be used. The research has practical value because it can be used to implement the applied method at training ranges in the contemporary military era. Bangladesh Journal of Medical Science Vol. 22 No. 03 July’23 Page : 573-584

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Analysis of Biometric Identification Methods Used in Auto-Proctoring

Bekenova,

Gumarova

et al. 2024

Springer Geography

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Efficient Facial Expression Recognition System Based on Geometric Features Using Neural Network

Cited by 7 publications

References 12 publications

Recognizing Facial Expressions Across Cultures Using Gradient Features

Recognizing Facial Expressions Across Cultures Using Gradient Features

Procedure for Identifying Negative Emotional States in Military Personnel

Analysis of Biometric Identification Methods Used in Auto-Proctoring

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