http://iet.metastore.ingenta.com
1887

Multi-stage age estimation using two level fusions of handcrafted and learned features on facial images

Multi-stage age estimation using two level fusions of handcrafted and learned features on facial images

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Age estimation from facial images is an important application of biometrics. In contrast to other facial variations like occlusions, illumination, misalignment and facial expressions, ageing variation is affected by human genes, environment, lifestyle and health which make age estimation a challenging task. In this study, the authors propose a new age estimation system which exploits multi-stage features from a generic feature extractor, a trained convolutional neural network (CNN), and precisely combined these features with a selection of age-related handcrafted features. This method utilises a decision-level fusion of estimated ages by two different approaches; the first one uses feature-level fusion of different handcrafted local feature descriptors for wrinkle, skin and facial component, while the second one uses score-level fusion of different feature layers of a CNN for its age estimation. Experiments on the publicly available MORPH-Album-2 and FG-NET databases prove the effectiveness of the novel method. Moreover, an additional experimental study on AgeDB database demonstrates that the proposed method is comparable with the best state-of-the-art system for age estimation using in-the-wild age databases.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5141
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5141
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address