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面部识别和分析:以机器学习为基础的企业心理健康管理途径

Zicheng Zhang^1,2, Tianshu Zhang³, Jie Yang²

¹School of Modern Post, Nanjing University of Posts and Telecommunications, Nanjing, China.

|April 29, 2025

概括

这项研究开发了一种基于面部的情绪识别系统,以实时监控员工的幸福感. 该系统准确地识别情绪状态,有助于积极的企业心理健康管理和支持.

科学领域:

计算机科学计算机科学
心理学心理学心理学
组织行为组织行为

背景情况:

员工的情感福祉对于工作场所的生产力,人际关系和稳定性至关重要.
评估情绪状态的传统方法往往是主观的,缺乏实时能力.
这项研究解决了客观,持续监测员工情绪的需要.

研究的目的:

引入创新的面部基于情绪识别系统,实时监测员工情绪状态.
通过技术干预来加强企业心理健康管理.
在组织中建立可扩展,情感意识的支持系统的基础.

主要方法:

使用RetinaFace用于面部检测,Dlib用于特征提取,VGG16用于微表情分类.
开发了一个10维的情感特征向量,并利用K-Nearest Neighbors进行异常检测.
应用基于3σ的风险评估方法来评估情绪异常,并使用数据增强来提高模型性能.

主要成果:

与MobileNet和ResNet50.0相比,VGG16模型在情绪分类方面表现出更高的准确性,精度和回忆力.
数据增强技术显著提高了微表达式识别在各种表达式中的性能和稳定性.
经验分析证实了该系统在分类情绪方面的高准确性.

结论:

基于面部的情绪识别系统有效监控员工的情绪趋势,并识别企业环境中的潜在风险.
该系统为早期干预提供了可操作的见解,有助于改善企业心理健康.
这项技术为组织环境中的基于情感的支持系统提供了可扩展的解决方案.

关键词:

情绪识别情绪识别公司心理健康深度学习是一种深度学习.面部分析面部分析微表达的分类是微表达的分类.

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