Identification of Parameters in 2D-FEM of Valve Piping System within NPP Utilizing Seismic Response

Xue, Ruiyuan; Yu, Shurong; Xi-heng, Zhang

doi:10.32604/cmc.2020.011340

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Face Detection and Emotion Recognition System Structure1

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2022

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“…It is critical to retain information with high stability and discrimination while also maintaining a high level of stability during this process. To identify a person's face, a number of unique characteristics are utilized [ 39 ].…”

Section: Face Detection and Emotion Recognition System Structurementioning

confidence: 99%

Methods for Facial Expression Recognition with Applications in Challenging Situations

Pise

Alqahtani

Verma

et al. 2022

Computational Intelligence and Neuroscience

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In the last few years, a great deal of interesting research has been achieved on automatic facial emotion recognition (FER). FER has been used in a number of ways to make human-machine interactions better, including human center computing and the new trends of emotional artificial intelligence (EAI). Researchers in the EAI field aim to make computers better at predicting and analyzing the facial expressions and behavior of human under different scenarios and cases. Deep learning has had the greatest influence on such a field since neural networks have evolved significantly in recent years, and accordingly, different architectures are being developed to solve more and more difficult problems. This article will address the latest advances in computational intelligence-related automated emotion recognition using recent deep learning models. We show that both deep learning-based FER and models that use architecture-related methods, such as databases, can collaborate well in delivering highly accurate results.

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Section: Face Detection and Emotion Recognition System Structurementioning

confidence: 99%