面向高效面部表情识别系统的各种深度学习算法综述

Rudranath Banerjee, S. De, Shouvik Dey
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引用次数: 1

摘要

面部表情(FE)包含了人的情绪和身体状态的信息。在过去的几年里,FE识别(FER)已经成为一个有利的研究领域。FER是非言语意图的主要处理技术,是计算机视觉与人工智能领域的重要发展方向。作为一种新颖的机器学习理论,深度学习(Deep learning, DL)不仅突出了学习模型的深度,而且强调了特征学习(Feature learning, FL)对网络模型的重要性,并在特征学习方面取得了一些研究成果。本文主要从最新的FE提取算法和以DL为中心的FE提取算法两方面考察了目前的研究现状。从最近的论文中收集的关于分类器的研究为研究人员提供了对分类器特有特征的更有力和可靠的理解。在调查的最后,除了在即将到来的未来需要解决的机会之外,几乎没有什么问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on Various Deep Learning Algorithms for an Efficient Facial Expression Recognition System
Facial Expression (FE) encompasses information concerning the emotional together with the physical state of a human. In the last few years, FE Recognition (FER) has turned out to be a propitious research field. FER is the chief processing technique for non-verbal intentions, and also it is a significant and propitious computer vision together with the artificial intelligence field. As a novel machine learning theory, Deep Learning (DL) not only highlights the depth of the learning model but also emphasizes the significance of Feature Learning (FL) for the network model, and it has made several research achievements in FER. Here, the present research states are examined typically from the latest FE extraction algorithm as well as the FER centered on DL. The research on classifiers gathered from recent papers discloses a more powerful as well as reliable comprehending of the peculiar traits of classifiers for research fellows. At the ending of the survey, few problems in addition to chances that are required to be tackled in the upcoming future are presented.
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