Hybrid Age Estimation Using Facial Images

Age estimation determines a person’s age or age group using facial images and has many real-world applications. This paper investigates various algorithms used to improve age estimation. A combination of features and classifiers are compared. A database of facial images is trained to extract feature...

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Bibliographic Details
Published inImage Analysis and Recognition Vol. 9164; pp. 239 - 246
Main Authors Reade, Simon, Viriri, Serestina
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Face Image
Feature Vector
Image processing
Linear Discriminant Analysis
Local Binary Pattern
Pattern recognition
Principle Component Analysis
Online AccessGet full text
ISBN3319208004
9783319208008
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-20801-5_26

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Summary:Age estimation determines a person’s age or age group using facial images and has many real-world applications. This paper investigates various algorithms used to improve age estimation. A combination of features and classifiers are compared. A database of facial images is trained to extract features using algorithms such as local binary patterns (LBP), active shape models and histogram of oriented gradients (HOG). The age estimation is done using three age groups: child, adult, senior. The ages are classified using support vector machine (SVM), K-nearest neighbour (KNN), gradient boosting tree (GBT). The age estimation model is evaluated using the FG-NET aging database obtaining positive results of 82 % success rate.
ISBN:3319208004
9783319208008
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-20801-5_26