Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80448
Title: Strategic decision-making learning from label distributions : an approach for facial age estimation
Authors: Wang, Han
Zhao, Wei
Keywords: Strategic Decision-making
Label Distribution Learning
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2016
Source: Zhao, W., & Wang, H. (2016). Strategic Decision-Making Learning from Label Distributions: An Approach for Facial Age Estimation. Sensors, 16(7), 994-. doi:10.3390/s16070994
Series/Report no.: Sensors
Abstract: Nowadays, label distribution learning is among the state-of-the-art methodologies in facial age estimation. It takes the age of each facial image instance as a label distribution with a series of age labels rather than the single chronological age label that is commonly used. However, this methodology is deficient in its simple decision-making criterion: the final predicted age is only selected at the one with maximum description degree. In many cases, different age labels may have very similar description degrees. Consequently, blindly deciding the estimated age by virtue of the highest description degree would miss or neglect other valuable age labels that may contribute a lot to the final predicted age. In this paper, we propose a strategic decision-making label distribution learning algorithm (SDM-LDL) with a series of strategies specialized for different types of age label distribution. Experimental results from the most popular aging face database, FG-NET, show the superiority and validity of all the proposed strategic decision-making learning algorithms over the existing label distribution learning and other single-label learning algorithms for facial age estimation. The inner properties of SDM-LDL are further explored with more advantages.
URI: https://hdl.handle.net/10356/80448
http://hdl.handle.net/10220/46538
ISSN: 1424-8220
DOI: 10.3390/s16070994
Schools: School of Electrical and Electronic Engineering 
Rights: © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

SCOPUSTM   
Citations 20

13
Updated on Jun 12, 2024

Web of ScienceTM
Citations 50

4
Updated on Oct 28, 2023

Page view(s)

284
Updated on Jun 15, 2024

Download(s) 50

79
Updated on Jun 15, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.