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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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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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基于CNN的面部表情识别,同时考虑阶级间和阶级内部的变化.

Trong-Dong Pham1, Minh-Thien Duong1, Quoc-Thien Ho1

  • 1Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea.

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这项研究引入了一种新的面部表情识别损失函数. 它通过更好地区分相似的情绪和增强不同情绪之间的差异来提高准确性.

关键词:
卷积神经网络是一种卷积神经网络.面部表情识别 面部表情识别类间的变化 类间的变化类内的变化 类内的变化功能损失的功能损失的功能.

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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:

  • 面部表情识别对于理解人类情绪和非语言线索至关重要.
  • 当前的面部识别技术往往忽视了深度学习模型中损失函数的重要性.
  • 现有的方法主要集中在新的网络架构上,忽视了损失函数优化.

研究的目的:

  • 为基于卷积神经网络 (CNN) 的面部表情识别引入新的损失功能.
  • 同时解决类间和类内部的变化,以提高识别准确度.
  • 通过优化损失函数来提高面部表情识别系统的性能.

主要方法:

  • 开发了一种新的损失函数,旨在通过将深层特征拉向它们的类中心来最大限度地减少类内变化.
  • 通过将深层特征从非对应的类中心推开,并最大限度地提高不同类中心之间的距离,增加了类间的变化.
  • 将拟议的损失函数集成到CNN架构中,用于面部表情识别任务.

主要成果:

  • 拟议的损失函数与基准数据集上的现有方法相比,显示出更高的性能.
  • 根据Cohn-Kanade Plus,Oulu-Casia,MMI和FER2013数据集进行评估,显示了显著的改进.
  • 有效地减少了类内变化和增加了类间变化,从而导致更强大的特征表示.

结论:

  • 新的损失函数在面部表情识别准确性和效率方面取得了重大进展.
  • 这种方法为训练情绪识别的深度学习模型提供了更有效的方法.
  • 该方法显示了对需要精确面部表情分析的现实应用的巨大潜力.