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Age Classification from Facial Images: Is Frontalization Necessary?

  • A. Báez-SuárezEmail author
  • C. NikouEmail author
  • J. A. Nolazco-FloresEmail author
  • I. A. KakadiarisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)

Abstract

In the majority of the methods proposed for age classification from facial images, the preprocessing steps consist of alignment and illumination correction followed by the extraction of features, which are forwarded to a classifier to estimate the age group of the person in the image. In this work, we argue that face frontalization, which is the correction of the pitch, yaw, and roll angles of the headpose in the 3D space, should be an integral part of any such algorithm as it unveils more discriminative features. Specifically, we propose a method for age classification which integrates a frontalization algorithm before feature extraction. Numerical experiments on the widely used FGnet Aging Database confirmed the importance of face frontalization achieving an average increment in accuracy of 4.43%.

Keywords

Image Resolution Textural Feature Facial Image Local Binary Pattern Convolutional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work has been funded in part by the Mexican National Council for Science and Technology (CONACYT) scholarship 328083 and by the UH Hugh Roy and Lillie Cranz Cullen Endowment Fund. The authors acknowledge the use of the Maxwell/Opuntia Cluster and the support of the Center of Advanced Computing and Data Systems at the University of Houston to carry out the research presented herein. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the sponsors.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Computational Biomedicine Lab, Department of Computer ScienceUniversity of HoustonHoustonUSA
  2. 2.Department of Computer ScienceITESM Campus MonterreyMonterreyMexico

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