英语翻译Framework for reliable,real-time facial expression recognition for low resolution images1.IntroductionCommunication in any form i.e.verbal or non-verbal is vital to complete various routine tasks and plays a significant role in daily life

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英语翻译Frameworkforreliable,real-timefacialexpressionrecognitionforlowresolutionimages1.IntroductionCom

英语翻译Framework for reliable,real-time facial expression recognition for low resolution images1.IntroductionCommunication in any form i.e.verbal or non-verbal is vital to complete various routine tasks and plays a significant role in daily life
英语翻译
Framework for reliable,real-time facial expression recognition for low resolution images
1.Introduction
Communication in any form i.e.verbal or non-verbal is vital to complete various routine tasks and plays a significant role in daily life.Facial expression is the most effective form of non-verbal communication and it provides a clue about emotional state,mindset and intention (Ekman,2001 ).Human visual system (HVS) decodes and analyzes facial expressions in real time despite having limited neural resources.As an explanation for such performance,it has been proposed that only some visual inputs are selected by considering ‘‘salient regions’’ (Zhaoping,2006 ),where ‘‘salient’’ means most noticeable or most important.
2.For computer vision community it is a difficult task to automatically recognize facial expressions in real-time with high reliability.Variability in pose,illumination and the way people show expressions across cultures are some of the parameters that make this task difficult.Low resolution input images makes this task even harder.Smart meeting,video conferencing and visual surveillance are some of the real world applications that require facial expression recognition system that works adequately on low resolution images.Another problem that hinders the development of such system for real world application is the lack of databases with natural displays of expressions (Valstar and Pantic,2010 ).There are number of publicly available benchmark databases with posed displays of the six basic emotions (Ekman,1971 ) exist but there is no equivalent of this for spontaneous basic emotions.While,it has been proved that Spontaneous facial expressions differ substantially from posed expressions (Bartlett et al.,2002 ).In this work,we propose a facial expression recognition system that caters for illumination changes and works equally well for low resolution as well as for good quality/high resolution images.We have tested our proposed system on spontaneous facial expressions as well and recorded encouraging results.

英语翻译Framework for reliable,real-time facial expression recognition for low resolution images1.IntroductionCommunication in any form i.e.verbal or non-verbal is vital to complete various routine tasks and plays a significant role in daily life
前面还有一句话你没写完不能单独翻译,对低分辨率图像进行实时面部表情识别.
1、简介
语言或者非语言的任何一种交流形式在对完成各种日常工作很至关重要,交流形式在日常生活中发挥着重要的作用.面部表情是非语言交际的最有效的形式,它是揭露出人们情绪状态,心态和意愿的一种线索（艾克曼,2001）.人类视觉系统（HVS）在有限神经资源的条件下解码和分析出了的实时面部表情.对于这样的表现说明只有在下面的情况下才能分析出,人们已提出只有一些视觉输入被选择到大脑的'突出地区”（昭平,2006）,其中“'突出”意味着显著的或最重要的区域.
2.对于计算机视觉社区来说自动识别实时可靠的面部表情是一个困难的任务.变化的动作,光照和不同文化人们的面部表情等这些因素让它变成一个困难的任务.低分辨率输入图像使这个任务更难.智能会议,视频会议和视频监控等这些真实世界应用程序需要面部表情识别系统充分工作于低分辨率的图像.另一个阻碍了对真实世界应用系统发展的问题是表情自然显示数据库的缺乏（2010 valstar和潘迪克,）这里有许多关于六种基本情绪（艾克曼,1971）显示的公开可用的数据库,但是没有对应的自发性情绪数据.同时,它还证明自发的面部表情不同于本质上的表情.（等Bartlett人,2002）.在这项工作中,我们提出了一个面部表情识别系统可以适应光照变化,同样适用于低分辨率以及/高分辨率的图像.我们已经测试出了我们提出的自发性面部表情系统并取得了鼓舞人心的结果.

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框架的可靠性，对低分辨率图像的实时面部表情识别
1。景区简介
以任何形式即言语或非言语沟通是完成各种日常任务至关重要，在日常生活中发挥着重要的作用。面部表情是非语言交际的最有效的形式，它提供了一种情绪状态的线索，心态和意愿（艾克曼，2001）。人类视觉系统（HVS）的解码和分析的实时面部表情，尽管有限的神经资源。对于这样的表现说明，它已被提出，只有一些视觉输入所考虑的'salient地区选择”（昭平，2006），其中“'salient”意味着最引人注目的或最重要的。
2。计算机视觉社区是一个困难的任务自动识别面部表情的实时性与可靠性高。变异的姿势，照明和表达的跨文化的人都是一些使这个任务参数的困难的方法。低分辨率输入图像，使这个任务更难。智能会议，视频会议和视频监控的一些真实世界的应用程序需要的面部表情识别系统，充分对低分辨率的图像。另一个问题，阻碍了对现实世界的应用系统开发具有表达自然显示数据库的缺乏（2010 valstar和潘迪克，）。有了公开的基准数据库显示的六种基本情绪的数量（艾克曼，1971）的存在，但没有对应的基本情绪的自发。同时，它已被证明，自发的面部表情不同基本上从构成表达式（Bartlett等人。，2002）。在这项工作中，我们提出了一个面部表情识别系统，适应光照变化，同样适用于低分辨率以及质量/高分辨率的图像很好。我们已经测试了我们所提出的系统自发的面部表情以及记录的令人鼓舞的结果。

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