Hand-over-Face Gesture based Facial Emotion Recognition using Deep Learning

Niti Naik, M. Mehta
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引用次数: 6

Abstract

Facial emotion recognition is significant for several applications such as human-computer interaction, video surveillance and robotics. Occlusion covering the face is a barrier in clearly recognizing facial emotion. Hence, in early researches, the hand-over-face occlusion is either removed or ignored while recognizing emotion. However, hand-over-face (hand gesture) along with facial expression represents an emotion. The existing hand-over-face gesture based emotion recognition methods identify merely basic emotions due to coding schema with limited hand gestures. In this paper, we propose a new hand-over-face gesture based emotion recognition method that includes coding schema with more hand gestures and uses deep learning. Specifically, we use Convolutional Neural Network (CNN) that eliminates the need for manual feature extraction and extracts more class specific features automatically. Moreover, we use Recurrent Neural Network (RNN) to recursively learn the features and classify them into more advanced emotion categories. Hence, our proposed method identifies more advanced emotions such as confident, making decision, scared, ashamed and ok sign along with basic emotions.
基于手势的深度学习面部情感识别
面部情感识别在人机交互、视频监控和机器人等领域具有重要意义。遮挡面部是识别面部情绪的障碍。因此,在早期的研究中,在识别情绪时,手遮住脸的遮挡要么被移除,要么被忽略。然而,手over-face(手势)和面部表情一起代表一种情绪。现有的基于手势的情绪识别方法由于手势编码模式的限制,只能识别基本的情绪。在本文中,我们提出了一种新的基于手势的情感识别方法,该方法包括包含更多手势的编码模式并使用深度学习。具体来说,我们使用卷积神经网络(CNN),它消除了手动特征提取的需要,并自动提取更多的类特定特征。此外,我们使用递归神经网络(RNN)递归学习特征,并将其分类到更高级的情感类别。因此,我们提出的方法可以识别更高级的情绪,如自信、做出决定、害怕、羞愧和ok,以及基本的情绪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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