Facial Expression Monitoring System Using PCA-Bayes Classifier

Ching Yee Yong, R. Sudirman, K. Chew
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引用次数: 9

Abstract

In order to endow a machine with an emotional intelligence is a challenging research issue and become one that has been of growing importance to those working in human-computer interaction. This study presents the framework of a special session to study and investigate the best techniques for emotion recognition, validation and analysis of expressivity in human-computer interaction, based on the common physiological background. A PCA-Bayes classifier (PCABC) was proposed in this study for facial recognition problem. The session is primarily concerned with visual emotion analysis; the analysis of physiological signals serves as a complement to this modality. Signal is taken from a different aspect of the physiology and visual. The signal will go through a process of elimination votes in order to extract better signal features. It is shown that the PCABC can perform much better than Least Mean Square (LMS) classifier. Psychological backgrounds will be studied to obtain good signal.
基于PCA-Bayes分类器的面部表情监测系统
赋予机器情感智能是一个具有挑战性的研究问题,对于那些从事人机交互工作的人来说,这一问题越来越重要。本研究提出了一个特别会议的框架,以研究和调查人机交互中情感识别,验证和分析表现力的最佳技术,基于共同的生理背景。本研究提出一种PCA-Bayes分类器(PCABC)用于人脸识别问题。会议主要关注视觉情感分析;对生理信号的分析是对这种模式的补充。信号来自生理和视觉的不同方面。为了提取更好的信号特征,信号将经过一个消除投票的过程。结果表明,PCABC分类器比最小均方(LMS)分类器具有更好的分类性能。心理背景将被研究,以获得良好的信号。
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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