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相关概念视频

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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Muscles for Facial Expressions01:14

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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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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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相关实验视频

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Deep Neural Networks for Image-Based Dietary Assessment
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基于深度多重学习的面部年龄识别.

Huiying Zhang1, Jiayan Lin1, Lan Zhou1

  • 1Pujiang Institute, Nanjing Tech University, Nanjing 211200, China.

Mathematical biosciences and engineering : MBE
|March 29, 2024
PubMed
概括

这项研究引入了深度多重学习 (DML),通过减少冗余特征来改进面部年龄识别. DML提高了年龄估计任务的准确性,优于现有的方法.

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:

  • 面部年龄识别对于现实应用至关重要.
  • 目前用于面部年龄识别的深度学习方法经常提取多余的特征,阻碍性能.
  • 高维面部数据为准确的年龄估计带来了挑战.

研究的目的:

  • 为有效的面部年龄识别提出一种新的深度多重学习 (DML) 方法.
  • 通过选择相关的与年龄相关的特征来提高年龄估计的准确性.
  • 解决基于深度学习的面部年龄识别中冗余特征提取的问题.

主要方法:

  • 深度学习被用来提取高维面部特征.
  • 使用多重学习从提取的高维数据中选择与年龄相关的特征.
  • 拟议的DML方法在MORPH和FG-NET数据集上得到了验证.

主要成果:

  • 深度多重学习 (DML) 方法在MORPH数据集上实现了1.60的平均绝对误差 (MAE).
  • 在FG-NET数据集上,DML方法实现了2.48的平均绝对误差 (MAE).
  • 与最先进的面部年龄识别方法相比,DML在准确度上表现出显著的改进.
关键词:
年龄识别识别器.卷积神经网络的神经网络.深度学习是一种深度学习.功能提取 特性提取多元学习学习多元学习平均绝对误差是什么意思

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结论:

  • 深度多重学习 (DML) 有效地减少冗余特征,以改善面部年龄识别.
  • 拟议的DML方法在年龄估计任务中提供了卓越的性能.
  • DML代表了面部年龄识别领域的一个有前途的进步.