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相关概念视频

Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Aggregates Classification01:29

Aggregates Classification

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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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
348
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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Classification of Systems-II01:31

Classification of Systems-II

179
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
179
Classification of Systems-I01:26

Classification of Systems-I

219
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
219
Labeling Emotion01:20

Labeling Emotion

184
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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相关实验视频

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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强大的人类面部情绪分类使用三重损失基于深度CNN特征和SVM.

Irfan Haider1, Hyung-Jeong Yang1, Guee-Sang Lee1

  • 1Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 500-757, Republic of Korea.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

这项研究引入了一种新的方法来检测人类面部情绪,使用定制的ResNet18模型与三重损失和SVM分类. 该方法在基准数据集上实现了高精度,改善了面部情绪识别性能.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 人类面部情绪检测是复杂的,因为高的阶级间差异和多样化的情绪表达.
  • 准确的面部情绪分类仍然是现有的机器学习模型面临的挑战.

研究的目的:

  • 为准确的人类面部情绪分类提出一种新而聪明的方法.
  • 为了提高面部情绪识别系统的性能.

主要方法:

  • 开发了一个定制的ResNet18模型,与三重损失函数 (TLF) 集成.
  • 使用转移学习,然后使用支持向量机 (SVM) 分类.
  • 使用RetinaFace进行面部检测和提取,ResNet18使用三重损失对剪切面部进行训练.

主要成果:

  • 与最先进的 (SoTA) 方法相比,拟的方法在JAFFE和MMI数据集上取得了更高的性能.
  • 在JAFFE和MMI数据集上分别记录了98.44%和99.02%的准确性,对七种情绪.
  • 对于FER2013和AFFECTNET数据集,还需要进行进一步的微调.

结论:

关键词:
在ResNet18中使用ResNet18在SVM中,SVM是SVM.情绪的分类 情绪的分类转移学习转移学习三倍损失的三倍损失.

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  • 将三重损失与ResNet18和SVM集成,为面部情绪分类提供了一个强大的管道.
  • 开发的方法显示了在推进基于计算机视觉的情感识别领域的巨大潜力.
  • 未来的工作将集中在优化更具挑战性的数据集的方法,如FER2013和AFFECTNET.