问题描述：
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.
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.
问题解答：
我来补答展开全文阅读