Recognition of Facial Stress System using Machine Learning with an Intelligent Alert System

S. K. Suba Raja, Durai Arumugam S S L, R. P. Kumar, J. Selvakumar
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引用次数: 1

Abstract

The primary component in this paper is to investigate the facial emotional states and EEG indicators, especially in pressure, for the duration of the interplay with games. The proposed paper identifies sure precise expressions in game enthusiasts whose facial feelings are segmented frames were separated into special areas, then the mind indicators are classified based on their frequencies, ranges are also analysed, and the signal value is found.Decided on facial features are extracted from the localized areas, used fuzzy c-means class, and directed onto an emotion space. Then the EEG sign price is evaluated with the brink fee. After that, the strain data can be ship thru by using a SMS alert by using GSM module and buzzer alerts the use of Arduino micro controller. The cease results are accurate and robust.
基于机器学习和智能警报系统的面部压力识别
本文的主要组成部分是研究面部情绪状态和EEG指标,特别是在与游戏相互作用的持续时间内的压力。本文对游戏爱好者的面部表情进行了精确的识别,并将其划分为特定的区域,然后根据其频率对其进行分类,分析其范围,并找到信号值。从局部区域提取确定的面部特征,采用模糊c均值类,并定向到情感空间。然后用边缘费对EEG信号价格进行评估。之后,可以通过GSM模块使用短信警报和使用Arduino微控制器使用蜂鸣器警报来发送应变数据。实验结果准确,鲁棒性好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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