An Online Extraction Algorithm for Image Feature Information Based on Convolutional Neural Network

Author:

Wei Dahuan1ORCID

Affiliation:

1. Beihai Vocational College, BeiHai, Guangxi, China

Abstract

As a result of fast technological improvement and the rise of online social media, image data have grown rapidly. Because of their rich content and intuitiveness as one of the key modes of people's daily communication, as a result, images are often used as communication vehicles. When it comes to image recognition, picture feature extraction is a critical stage, and the effect of image feature extraction directly impacts the effectiveness of image recognition. Furthermore, feature extraction is a key factor to consider that influences picture recognition accuracy. Unfortunately, due to the effects of individual variations and lighting, certain elements that are significantly connected to alterations in the image are hard to extract. As a result, features that appropriately show the changing interface are urgently needed. For this purpose, this research proposes an expression identification approach based on a deep convolutional neural network for the job of facial expression recognition under online picture feature information extraction. It uses the VGG19 and Resnet18 to recognize and classify facial expressions. After that, the DCNs have combined feature extraction and classification into a single network using deep convolutional neural networks (DCNs). The proposed model is compared to the most recent approach in the context of the FER2013 and CK + databases. The experimental results reveal that this method outperforms the competition, and the amount of useful image feature information that can be extracted is substantial.

Funder

Guangxi Young and Middle-Aged Teachers’ Basic Scientific Research Ability Improvement

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

Reference21 articles.

1. Overview of artificial neural network;Q. Zhang;Journal of Liaoning Economic Vocational and Technical College Liaoning Economic Management Cadre College,2010

2. Information theoretic feature selection for high dimensional metagenomic data;G. Ditzler

3. A Feature Subset Selection Algorithm Automatic Recommendation Method

4. Variable noise and dimensionality reduction for sparse Gaussian processes;E. Snelson

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3