Classification of emotions on images through convolutional neural networks as a method of preventing secondary alexithymia

Alejandra TREJO-FRÍAS, Paulina RICO-GARCÍA, Diego Ángel VILLAFUERTE-LUCIO, Christian Padilla-Navarro
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Abstract

Alexithymia can be defined as the inability to verbalize affective states. One of its main causes lies in the lack of learning of emotions during childhood and can prevail until adulthood. Its identification at an early age can solve problems such as depression and cutting that, in severe cases, can lead to suicide. The present investigation shows the implementation of two convolutional neural networks for the classification of emotions through images.
利用卷积神经网络对图像进行情绪分类,作为预防继发性述情障碍的一种方法
述情障碍可以定义为无法用语言表达情感状态。其主要原因之一是在童年时期缺乏对情绪的学习,并可能一直持续到成年。在早期发现它可以解决抑郁症和割伤等问题,在严重的情况下,这些问题可能导致自杀。本研究展示了两个卷积神经网络的实现,用于通过图像对情绪进行分类。
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