Next Article in Journal
Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
Next Article in Special Issue
Detection and Classification of Overlapping Cell Nuclei in Cytology Effusion Images Using a Double-Strategy Random Forest
Previous Article in Journal
Lamb Wave Local Wavenumber Approach for Characterizing Flat Bottom Defects in an Isotropic Thin Plate
Previous Article in Special Issue
Planning Lung Radiotherapy Incorporating Motion Freeze PET/CT Imaging
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(9), 1601;

Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation

School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Current address: Ligong Road #600, Houxi Town, Jimei District, Xiamen 361024, Fujian Province, China.
Author to whom correspondence should be addressed.
Received: 9 August 2018 / Revised: 26 August 2018 / Accepted: 3 September 2018 / Published: 10 September 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
Full-Text   |   PDF [1699 KB, uploaded 10 September 2018]   |  


Image-based age estimation is a challenging task since there are ambiguities between the apparent age of face images and the actual ages of people. Therefore, data-driven methods are popular. To improve data utilization and estimation performance, we propose an image-based age estimation method. Theoretically speaking, the key idea of the proposed method is to integrate multi-modal features of face images. In order to achieve it, we propose a multi-modal learning framework, which is called Multiple Network Fusion with Low-Rank Representation (MNF-LRR). In this process, different deep neural network (DNN) structures, such as autoencoders, Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and so on, can be used to extract semantic information of facial images. The outputs of these neural networks are then represented in a low-rank feature space. In this way, feature fusion is obtained in this space, and robust multi-modal image features can be computed. An experimental evaluation is conducted on two challenging face datasets for image-based age estimation extracted from the Internet Move Database (IMDB) and Wikipedia (WIKI). The results show the effectiveness of the proposed MNF-LRR. View Full-Text
Keywords: age estimation; multi-modal features; deep learning; low-rank representation age estimation; multi-modal features; deep learning; low-rank representation

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Hong, C.; Zeng, Z.; Wang, X.; Zhuang, W. Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation. Appl. Sci. 2018, 8, 1601.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top