Human age estimation framework using different facial parts

Human age estimation framework using different facial parts

Human age estimation from facial images has a wide range of real-world applications in human computer interaction (HCI). In this paper, we use the bio-inspired features (BIF) to analyze different facial parts: (a) eye wrinkles, (b) whole internal face (without forehead area) and (c) whole face (with...

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Bibliographic Details
Main Authors: Mohamed Y. El Dib, Hoda M. Onsi
Format: Article
Language:English
Published: Elsevier 2011-03-01
Series:Egyptian Informatics Journal
Subjects:
Age estimation
Bio-inspired features
Support vector machine
Support vector regression
Online Access:http://www.sciencedirect.com/science/article/pii/S111086651100003X
Description
Summary:Human age estimation from facial images has a wide range of real-world applications in human computer interaction (HCI). In this paper, we use the bio-inspired features (BIF) to analyze different facial parts: (a) eye wrinkles, (b) whole internal face (without forehead area) and (c) whole face (with forehead area) using different feature shape points. The analysis shows that eye wrinkles which cover 30% of the facial area contain the most important aging features compared to internal face and whole face. Furthermore, more extensive experiments are made on FG-NET database by increasing the number of missing pictures in older age groups using MORPH database to enhance the results.
ISSN:1110-8665

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