Wang, Haoyi, Sanchez Silva, Victor, Ouyang, Wanli and Li, Chang-Tsun (2020) Using age information as a soft biometric trait for face image analysis. In: Jiang, R. and Li, C. T. and Crookes, D. and Meng, W. and Rosenberger, C., (eds.) Deep Biometrics. Unsupervised and Semi-Supervised Learning . Springer, pp. 1-20. ISBN 9783030325824

PDF WRAP-using-age-information-soft-biometric-trait-face-image-analysis-Li-2020.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 29 January 2022. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (4Mb)

Request Changes to record.

Abstract

Soft biometrics refers to a group of traits that can provide some information about an individual but are inadequate for identification or recognition purposes. Age, as an important soft biometric trait, can be inferred based on the appearance of human faces. However, compared to other facial attributes like race and gender, age is rather subtle due to the underlying conditions of individuals (i.e., their upbringing environment and genes). These uncertainties make age-related face image analysis (including age estimation, age synthesis and age-invariant face recognition) still unsolved. Specifically, age estimation is concerned with inferring the specific age from human face images. Age synthesis is concerned with the rendering of face images with natural ageing or rejuvenating effects. Age-invariant face recognition involves the recognition of the identity of subjects correctly regardless of their age. Recently, thanks to the rapid development of machine learning, especially deep learning, age-related face image analysis has gained much more attention from the research community than ever before. Deep learning based models that deal with age-related face image analysis have also significantly boosted performance compared to models that only use traditional machine learning methods, such as decision trees or boost algorithms. In this chapter, we first introduce the concepts and theory behind the three main areas of age-related face image analysis and how they can be used in practical biometric applications. Then, we analyse the difficulties involved in these applications and summarise the recent progress by reviewing the state-of-the-art methods involving deep learning. Finally, we discuss the future research trends and the issues that are not addressed by existing works. We also discuss the relationship among these three areas and show how solutions within one area can help to tackle issues in the others.

Item Type:	Book Item
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Biometric identification, Human face recognition (Computer science), Computer vision, Image processing -- Digital techniques, Image analysis
Series Name:	Unsupervised and Semi-Supervised Learning
Publisher:	Springer
ISBN:	9783030325824
Book Title:	Deep Biometrics
Editor:	Jiang, R. and Li, C. T. and Crookes, D. and Meng, W. and Rosenberger, C.
Official Date:	2020
Dates:	Date Event 2020 Published 29 January 2020 Available 1 August 2019 Accepted
Date of first compliant deposit:	18 May 2020
Page Range:	pp. 1-20
DOI:	10.1007/978-3-030-32583-1_1
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Description:	.
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 690907 Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661
Related URLs:	Other Publisher

Request changes or add full text files to a record

Repository staff actions (login required)

View Item