Deep learning-based emotion detection

Yuwei Chen, Jia-Zhou He
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引用次数: 3

Abstract

Since the deep learning methods used in current face recognition do not balance well between recognition rate and recognition speed, the present work proposed a face expression recognition model based on multilayer feature fusion with lightweight convolutional networks. The model is tested on two commonly used real expression datasets, FER- 2013 and AffectNet, the accuracy of ms_model_M is 74.35% and 56.67%, respectively, and the accuracy of the traditional MovbliNet model is 74.11% and 56.48% in the tests of these two datasets.
基于深度学习的情绪检测
鉴于当前人脸识别中使用的深度学习方法在识别率和识别速度之间没有很好的平衡,本文提出了一种基于多层特征融合和轻量级卷积网络的人脸表情识别模型。该模型在FER-2013和AffectNet两个常用的真实表达数据集上进行了测试,在这两个数据集的测试中,ms_model_M的准确率分别为74.35%和56.67%,传统MovbliNet模型的准确率为74.11%和56.48%。
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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