Classification of Sad Emotions and Depression Through Images Using Convolutional Neural Network (CNN)

Muhammad Fathur Prayuda
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Abstract

The human face has various functions, especially in expressing something. The expression shown has a unique shape so that it can recognize the atmosphere of the feeling that is being felt. The appearance of a feeling is usually caused by emotion. Research on the classification of emotions has been carried out using various methods. For this study, a Convolutional Neural Network (CNN) method was used which serves as a classifier for sad and depressive emotions. The CNN method has the advantage of preprocessing convolution so that it can extract a hidden feature in an image. The dataset used in this study came from the Facial expression dataset image folders (fer2013) where the dataset used for classification was taken with a ratio of 60% training and 40% validation with the results of the trained model of 60% total loss and 68% test accuracy.
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基于卷积神经网络(CNN)图像的悲伤情绪和抑郁分类
人脸有多种功能,尤其是在表达某些东西时。所显示的表情具有独特的形状,因此它可以识别所感受到的感觉的氛围。一种感觉的出现通常是由情绪引起的。关于情绪分类的研究已经用各种方法进行了。在本研究中,使用卷积神经网络(CNN)方法作为悲伤和抑郁情绪的分类器。CNN方法具有预处理卷积的优点,可以提取图像中的隐藏特征。本研究使用的数据集来自面部表情数据集图像文件夹(fer2013),其中用于分类的数据集以60%的训练和40%的验证比例进行,训练后的模型总损失为60%,测试准确率为68%。
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