利用深度学习模型改进优化情绪检测和分类。

IF 2.6 4区 工程技术 Q1 Mathematics
C Willson Joseph, G Jaspher Willsie Kathrine, Shanmuganathan Vimal, S Sumathi, Danilo Pelusi, Xiomara Patricia Blanco Valencia, Elena Verdú
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引用次数: 0

摘要

面部情绪识别(FER)在很大程度上用于分析人类情绪,以满足许多实时应用的需求,如计算机-人机界面、情绪检测、法医、生物识别和人机协作。然而,现有的方法大多无法以最低的错误率提供正确的预测。本文设计了一种创新的面部情绪识别框架,称为基于海象深度学习和肉毒杆菌特征选择网络的扩展海象深度学习(EWDL-BFSN),用于准确检测面部情绪。EWDL-BFSN 的主要目标是通过选择最佳特征和调整分类器的超参数,自动有效地识别面部情绪。梯度小波各向异性滤波器(GWAF)可用于 EWDL-BFSN 模型的图像预处理。此外,SqueezeNet 还可用于提取重要特征。然后使用改进的肉毒杆菌优化算法(IBoA)来选择最佳特征。最后,通过使用基于增强优化的核残差 50(EK-ResNet50)网络来完成 FER 和分类。与此同时,受自然启发的元启发式海象优化算法(WOA)被用来选择 EK-ResNet50 网络模型的超参数。EWDL-BFSN 模型通过公开的 CK+ 和 FER-2013 数据集进行了训练和测试。在实现过程中使用了 Python 平台,并对准确率、灵敏度、特异性和 F1 分数等各种性能指标与最先进的方法进行了分析。所提出的 EWDL-BFSN 模型在 CK+ 和 FER-2013 数据集上的总体准确率分别为 99.37% 和 99.25%,证明其在预测面部情绪方面优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved optimizer with deep learning model for emotion detection and classification.

Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods.

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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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