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Volume: 31 | Article ID: art00005
Emotion Recognition Using Convolutional Neural Networks
  DOI :  10.2352/ISSN.2470-1173.2019.8.IMAWM-402  Published OnlineJanuary 2019

Emotion has an important role in daily life, as it helps people better communicate with and understand each other more efficiently. Facial expressions can be classified into 7 categories: angry, disgust, fear, happy, neutral, sad and surprise. How to detect and recognize these seven emotions has become a popular topic in the past decade. In this paper, we develop an emotion recognition system that can apply emotion recognition on both still images and real-time videos by using deep learning. We build our own emotion recognition classification and regression system from scratch, which includes dataset collection, data preprocessing, model training and testing. Given a certain image or a real-time video, our system is able to show the classification and regression results for all of the 7 emotions. The proposed system is tested on 2 different datasets, and achieved an accuracy of over 80%. Moreover, the result obtained from realtime testing proves the feasibility of implementing convolutional neural networks in real time to detect emotions accurately and efficiently.

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Shaoyuan Xu, Yang Cheng, Qian Lin, Jan Allebach, "Emotion Recognition Using Convolutional Neural Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2019,  pp 402-1 - 402-9,

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