用神经网络进行图像分析进行情感识别的新趋势,系统性审查
Andrada-Livia Cîrneanu1, Dan Popescu1, Dragoș Iordache2
1Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
面部情绪识别 (FER) 系统使用深度神经网络进行准确的人类表情分析. 本综述强调卷积神经网络 (CNN) 是推动各种领域FER应用的关键.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 面部情绪识别 (FER) 对于教育,医疗保健和公共安全领域的应用至关重要.
- 深度神经网络 (DNN) 为像FER这样的图像分析任务提供先进的特征提取和计算效率.
- 准确检测和识别人类情绪表达对于有效部署FER系统至关重要.
研究的目的:
- 审查面部情绪识别 (FER) 的最新发展.
- 分析FER神经网络架构的演变,重点关注深度学习模型.
- 为了比较各种神经网络架构的性能,优势和局限性,特别是支持CNN.
主要方法:
- 对FER的神经网络模型的最新文献的审查.
- 神经网络架构 (CNN,RNN,GAN) 的历史和概念分析.
- 对面部表情和微表情识别数据集的检查.
主要成果:
- 卷积神经网络 (CNN) 被强调为FER任务的高度有效.
- 分析不同神经网络架构的关键元素,性能,优势和局限性.
- 识别可用的数据集用于培训和评估FER模型.
结论:
- 基于CNN的架构显示出对推进FER的重大前景.
- FER系统在医疗保健,教育,安全和社会物联网方面具有多样化的应用.
- 未来的研究应该解决未解决的问题,并探索FER技术的新发展.
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