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首页> 外文期刊>International journal of technology diffusion >Emotion Recognition Model Based on Facial Expressions,Ethnicity and Gender Using Backpropagation Neural Network
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Emotion Recognition Model Based on Facial Expressions,Ethnicity and Gender Using Backpropagation Neural Network

机译:反向传播神经网络的基于面部表情,种族和性别的情绪识别模型

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摘要

Many emotion recognition approaches are built using facial expressions, but few of them use both the ethnicity and gender as attributes. The authors have developed an approach based on Artificial Neural Networks (ANN) using backpropagation algorithm to recognize the human emotion throughfacial expressions, ethnicity andgender. Their approach has been tested by using MSDEFdataset, and found that there is a positive effect on the accuracy of the recognition of emotion if they use both the ethnic group and gender as inputs to the system. Although this effect is not significant, but considerable (Improvement rate reached 8%). The authors also found that females have more accurate emotion expression recognition than males and found that the gender increases the accuracy of emotion recognition. Regardless of the used dataset, the authors 'approach obtained better results than some research on emotion recognition. This could be due to various reasons such as the type of the selected features and consideration of race and gender.
机译:许多情感识别方法都是使用面部表情构建的,但很少有人同时使用种族和性别作为属性。作者开发了一种基于人工神经网络(ANN)的方法,该方法使用反向传播算法通过面部表情,种族和性别来识别人的情绪。他们的方法已经通过使用MSDEF数据集进行了测试,发现如果他们同时使用族裔和性别作为系统的输入,则会对情感识别的准确性产生积极影响。尽管此效果不明显,但可观(改进率达到8%)。作者还发现女性比男性具有更准确的情感表达识别能力,并且发现性别可以提高情感识别的准确性。无论使用哪种数据集,作者的方法都比情感识别研究获得更好的结果。这可能是由于各种原因造成的,例如所选功能的类型以及对种族和性别的考虑。

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