Volltext-Downloads (blau) und Frontdoor-Views (grau)

Aesthetic classification of face images based on convolutional neural network model

  • Aimed at the problem that the accuracy of face image classification in complex environment is not high, a network model F-Net suitable for aesthetic classification of face images is proposed. Based on LeNet-5, the model uses convolutional layers to extract facial image features in complex backgrounds, optimized parameters in the network model, and changes the number of convolutional layers and fully connected layer feature elements in the model. The experimental results show that the F-Net network model proposed in this paper has a face image classifation accuracy of 73% in complex environment background, which is better than other classical convolutional neural network classification models.

Download full text files

  • 2678.pdf
    eng

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Name:Rätsch, Matthias
DOI:https://doi.org/10.13338/j.issn.1674-649X.2019.06.014
ISSN:1674-649X
Erschienen in:Journal of Xi'an Polytechnic University
Publisher:Xi'an Polytechnic University
Place of publication:Xi'an
Document Type:Article
Language:English
Year of Publication:2019
Tag:LeNet-5; aesthetic classification; convolutional neural network; face recognition; image processing
Volume:33
Issue:6
Pagenumber:7
First Page:673
Last Page:678
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften
Open Access:Nein