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相关概念视频

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Muscles for Facial Expressions01:14

Muscles for Facial Expressions

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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Association Areas of the Cortex01:21

Association Areas of the Cortex

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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
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Nonconscious Mimicry01:13

Nonconscious Mimicry

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Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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相关实验视频

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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

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一个自动改进的面部表情识别,用于面具面孔.

Yasmeen ELsayed1, Ashraf ELSayed1,2, Mohamed A Abdou3

  • 1Faculty of Science, Alexandria University, Alexandria, Egypt.

Neural computing & applications
|June 5, 2023
PubMed
概括

这项研究介绍了一种混合深度学习模型,用于掩盖面部情绪识别. 这种新的方法显著提高了检测情绪的准确性,尽管面罩面临挑战.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 自动面部表情识别 (AFER) 对于社交互动至关重要.
  • 随着COVID-19大流行和强制性口罩的使用,AFER系统面临重大挑战.
  • 现有的机器学习技术难以识别面具面孔的情绪.

研究的目的:

  • 开发一个准确的AFER方法,用于面具面孔.
  • 改善面具条件下的基本情绪 (愤怒,幸福,悲伤,惊喜,蔑视,厌恶,恐惧) 的识别.

主要方法:

  • 开发了一种混合卷积神经网络 (CNN) 模型.
  • 局部二进制模式 (LBP) 被集成用于准确的特征提取,特别是面部蒙面区域.
  • 该模型在CK,CK+和M-LFW-FER数据集上进行了评估.

主要成果:

  • 提出的方法在识别面具面孔上的三个情绪方面达到70.76%的准确率.
  • 性能与现有的AFER技术相比进行了评估.
  • 与以前的方法相比,在准确度方面取得了显著的改进.

结论:

关键词:
卷积神经网络是一个卷积神经网络.面部表情识别 面部表情识别功能提取 功能提取当地二进制模式本地二进制模式

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  • 混合CNN-LBP模型为面罩的AFER提供了一个强大的解决方案.
  • 这一进步对于在后COVID-19时代保持社交互动和人机互动至关重要.
  • 进一步的研究可以探索更广泛的情感集和多样化的数据集.