Deep Learning based Facial Expression Recognition for Psychological Health Analysis

C. Jonitta Meryl, K. Dharshini, D. Sujitha Juliet, J. Akila Rosy, Sneha Sara Jacob
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引用次数: 8

Abstract

Facial Expression Recognition is known for its efficiency and its stimulating job in this automated world. Facial Expressions are the easiest way for human being to express their feelings. Facial expression plays a major role in communicating non-verbally. This paper summarizes the Facial Expression Recognition (FER) techniques based on deep learning. FER technique’s performance is compared based on the amount of expressions recognized and the difficulty of algorithms in CNN. FER 2013 database is been used here. Recently, the CNN (Convolutional Neural Networks) has gained the reputation within the field of deep learning owing to their effective design and also the ability to produce smart results without manual feature extraction from the raw information. This paper investigates the effectiveness of CNN with Radial Basis Function for expression recognition. The experimental results shows that the proposed method provide relatively better accuracy for FER 2013 dataset.
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基于深度学习的面部表情识别用于心理健康分析
在这个自动化的世界里,面部表情识别以其效率和令人兴奋的工作而闻名。面部表情是人类表达情感最简单的方式。面部表情在非语言交流中起着重要作用。综述了基于深度学习的面部表情识别技术。基于CNN中识别的表达式量和算法难度,比较了FER技术的性能。这里使用fer2013数据库。最近,CNN(卷积神经网络)由于其有效的设计以及无需从原始信息中手动提取特征即可产生智能结果的能力,在深度学习领域获得了声誉。研究了基于径向基函数的CNN在表情识别中的有效性。实验结果表明,该方法对fer2013数据集具有较好的精度。
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