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Analysing Emotions on Lecture Videos using CNN AND HOG (Workshop Paper)

机译:使用CNN和HOG分析讲课视频上的情绪(研讨会论文)

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Facial Expressions play a vital role in the process of recognizing emotions and also to have non-verbal communication as well as in identifying people. Emotional Recognition turns out to be very important in everyday life just next to the tone of voice. Novel applications in Human-Computer Interaction (HMI) have been enabled by this and in many other areas. Inevitably most of the recent research on this area focuses on Convolutional Neural Networks (CNN) for extraction of features and inference from those features. In this paper we used CNN algorithm along with Histogram of gradients (HOG) features for higher accuracy. We perform emotional analysis of lecturers from Impartus videos. By detecting their emotions from a sequence of videos during their coursework, we evaluate the feedback they are expected to receive at the end of the duration of their course.
机译:面部表情在识别情绪的过程中发挥着至关重要的作用,并且还具有非口头沟通以及识别人们。情绪识别在日常生活中,在旁边的声音中的日常生活中变得非常重要。人机交互(HMI)的新应用已通过此和许多其他领域启用。关于该领域的最近最近的大部分研究侧重于卷积神经网络(CNN),用于提取这些特征的特征和推断。在本文中,我们使用CNN算法以及梯度(HOG)特征的直方图,以获得更高的精度。我们对Ippartus视频进行了对讲师的情感分析。通过在课程期间从一系列视频中检测到他们的情绪,我们评估他们在课程期限结束时收到的反馈。

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