英译汉,不要机译,高手来Generally, facial expression recognition system consists of three steps: face detection, feature extraction and expression classification. The same has been shown in Fig. 1. In our framework we tracked face/salient fac

英译汉,不要机译,高手来Generally, facial expression recognition system consists of three steps: face detection, feature extraction and expression classification. The same has been shown in Fig. 1. In our framework we tracked face/salient fac
英译汉,不要机译,高手来
Generally, facial expression recognition system consists of three steps: face detection, feature
extraction and expression classification. The same has been shown in Fig. 1. In our framework we
tracked face/salient facial regions using Viola–Jones object detection algorithm (Viola and Jones, 2001 )
as it is the most cited and considered the fastest and most accurate pattern recognition method for face
detection (Kolsch and Turk, 2004 ). The second step in the framework is feature extraction, which is the
area where this study contributes. The optimal features should minimize within- class variations of
expressions , while maximize between class variations. If inadequate features are used, even the best
classifier could fail to achieve accurate recognition (Shan et al., 2009 ). Section 3presents the novel
method for facial features extraction which is based on human visual system (HVS). To study and under- stand HVS we performed psycho-visual experiment. Psycho-visual experimental study is briefly
described in Section 4. Expression classification or recognition is the last step in the pipeline. In
literature two different ways are prevalent to recognize expressions i.e.direct recognition of prototypic
expressions or recognition of expressions through facial action coding system (FACS) action units (AUs)
(Ekman and Friesen, 1978 ). In our proposed frame- work, which is described in Section 5we directly
classify six uni- versal prototypic expressions (Ekman, 1971 ). The performance of the framework is
evaluated for five different classifiers (from different families i.e. classification tree, instance based
learning,SVM, etc.) and results are presented in Section 6. Next section presents the brief literature
review for facial features extraction methods.

英译汉,不要机译,高手来Generally, facial expression recognition system consists of three steps: face detection, feature extraction and expression classification. The same has been shown in Fig. 1. In our framework we tracked face/salient fac
一般情况下,面部表情识别系统主要分为三个步骤：人脸检测,特征提取和表情分类.同样已经被示于图1.
在框架中,我们使用中提琴-琼斯目标检测算法（Viola-Jones,2001）跟踪的脸部/面部突出的地方,因为它是被引用最多的,并认为是最快,最准确的模式识别方法进行人脸检测（科隆— 土耳其人,2004） .
在框架中的第二个步骤是提取特征,是这个研究贡献的领域所在.
最优的特点,应尽量减少种类间的变化的表达式,以及种类之间的差异最大化.如果使用的功能不足,即使是最好的分类器可能无法实现准确的识别（Shan等人,2009）.
第3步骤介绍了新颖的面部特征提取是基于人类视觉系统（HVS）的方法.我们学习并理解HVS来进行心理视觉实验.心理视觉实验研究在第4节中简要地描述了表情分类或识别是管道中的最后一步.
在文献中,两种不同的方式很普遍承认表达式直接识别原型的,通过面部动作编码系（FACS）动作单元（AU）的表达式或认可的表达式（埃克曼和弗里森,1978）.
我们提出的框架工作在第5节,直接划分六个通用原型表达式（艾克曼,1971）.
该框架的性能被分为五种（来自不同的家庭,即分类树,基于实例的学习,支持向量机等）,结果在第6章介绍.下一节给出了简要的文献的面部特征提取方法检讨.
以上是机器翻译+本人自己的翻译
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面部表情识别系统通常分为三个步骤：面部识别，特征抽取，表情分类。图1展示了这三个步骤，我们在框架中使用Viola–Jones对象检测算法(Viola and Jones, 2001 )来跟踪面部或显著的面部区域，因为该算法是在面部检测中使用最为普遍，最快最精确的识别方法(Kolsch and Turk, 2004 )。框架中第二个步骤...

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这是试译，全部翻译您需要付费，同意的话可以随时联系我。
面部表情识别系统通常分为三个步骤：面部识别，特征抽取，表情分类。图1展示了这三个步骤，我们在框架中使用Viola–Jones对象检测算法(Viola and Jones, 2001 )来跟踪面部或显著的面部区域，因为该算法是在面部检测中使用最为普遍，最快最精确的识别方法(Kolsch and Turk, 2004 )。框架中第二个步骤是特征抽取，也是本研究的贡献领域。最优特征应该将组内变异最小化，同时将组间变异最大化。如果使用的特征不足，哪怕最好的分类器也无法实现精确识别(Shan et al., 2009 )。

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