Age regression from faces using random forests

Predicting the age of a person through face image analysis holds the potential to drive an extensive array of real world applications from human computer interaction and security to advertising and multimedia. In this paper the first application of the random forest for age regression is proposed. T...

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Published in2009 16th IEEE International Conference on Image Processing (ICIP) Vol. 2009; pp. 2465 - 2468
Main Authors Montillo, A., Haibin Ling
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.11.2009
Subjects
age regression
Aging
Application software
Face
Human computer interaction
Image databases
Labeling
learning
Learning systems
Performance evaluation
random forest
Spatial databases
Testing
Online AccessGet full text
ISBN9781424456536
1424456533
ISSN1522-4880
DOI10.1109/ICIP.2009.5414103

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Abstract Predicting the age of a person through face image analysis holds the potential to drive an extensive array of real world applications from human computer interaction and security to advertising and multimedia. In this paper the first application of the random forest for age regression is proposed. This method offers the advantage of few parameters that are relatively easy to initialize. Our method learns salient anthropometric quantities without a prior model. Significant implications include a dramatic reduction in training time while maintaining high regression accuracy throughout human development.
AbstractList Predicting the age of a person through face image analysis holds the potential to drive an extensive array of real world applications from human computer interaction and security to advertising and multimedia. In this paper the first application of the random forest for age regression is proposed. This method offers the advantage of few parameters that are relatively easy to initialize. Our method learns salient anthropometric quantities without a prior model. Significant implications include a dramatic reduction in training time while maintaining high regression accuracy throughout human development.
Predicting the age of a person through face image analysis holds the potential to drive an extensive array of real world applications from human computer interaction and security to advertising and multimedia. In this paper the first application of the random forest for age regression is proposed. This method offers the advantage of few parameters that are relatively easy to initialize. Our method learns salient anthropometric quantities without a prior model. Significant implications include a dramatic reduction in training time while maintaining high regression accuracy throughout human development.Predicting the age of a person through face image analysis holds the potential to drive an extensive array of real world applications from human computer interaction and security to advertising and multimedia. In this paper the first application of the random forest for age regression is proposed. This method offers the advantage of few parameters that are relatively easy to initialize. Our method learns salient anthropometric quantities without a prior model. Significant implications include a dramatic reduction in training time while maintaining high regression accuracy throughout human development.
Author Haibin Ling
Montillo, A.
AuthorAffiliation 1 University of Pennsylvania, Radiology; Rutgers University, CIS Dept, Philadelphia, PA USA
2 Computer and Information Science Department, Temple University, Philadelphia, PA, USA
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SubjectTerms age regression
Aging
Application software
Face
Human computer interaction
Image databases
Labeling
learning
Learning systems
Performance evaluation
random forest
Spatial databases
Testing
Title Age regression from faces using random forests
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